Methods of calculating differences of binding affinities between congeneric pairs of ligands by way of a displaced solvent functional

ABSTRACT

Described is a technique to exhaustively enumerate the thermodynamic properties of the water molecules solvating the active site of a protein in its apostate and calculate the relative binding affinities of congeneric compounds that bind to this protein. The subject matter includes sampling the configurations of the solvating water in the active site; extracting the thermodynamic information about the solvating water from these configurations by clustering the observed water configurations into regions of high water occupancy (e.g., “hydration sites”), computing the average system interaction energies of water molecules occupying the various hydrations sites, computing excess entropies of water molecules occupying the hydration sites; constructing a 3 dimensional hydration thermodynamics map of the protein active site; and computing relative binding affinities of congeneric ligands based on the principle that tighter binding ligands can displace more entropically structured and energetically depleted hydration sites from the active site into the bulk fluid.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of Ser. No. 11/982,783, filed Nov. 5,2007 now U.S. Pat. No. 7,756,674, which claims priority to U.S.Provisional Application Ser. No. 60/953,764, filed on Aug. 3, 2007,which is incorporated by reference herein, and from which priority isclaimed.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

The subject matter described herein was funded in part by federalgrants: subcontract NIH GM52018, NIH GM43340, UMD UMARY Z477901, and NSFCHE 06 13401. The United States Government may have certain rightsherein.

BACKGROUND

Understanding the underlying physics of the binding of small moleculeligands to protein active sites is one objective of computationalchemistry and biology. While a wide range of techniques exist forcalculating binding free energies, ranging from techniques that shouldbe accurate in principle (e.g., free energy perturbation (FEP) theory)to relatively simple approximations based on empirically derived scoringfunctions, no completely satisfactory and robust approach has yet beendeveloped. Furthermore, physical insight into the sources of bindingaffinity can be important for computing accurate numbers and can bevaluable in many areas (e.g., the design of pharmaceutical candidatemolecules).

It is widely believed that displacement of water molecules from theactive site by the ligand is a major source of binding free energy.Water molecules solvating protein active sites are often entropicallyunfavorable due to the orientational and positional constraints imposedby the protein surface, or energetically unfavorable due to the watermolecule's inability to form a full complement of hydrogen bonds whensolvating the protein surface. This leads to free energy gains when aligand that is suitably complementary to the active site displaces thesewaters into bulk solution, thus providing a relatively more favorableenvironment. FEP techniques can compute these free energy gainsexplicitly (within the accuracy of the force field used in thesimulations) but are computationally expensive.

This computational expense has been a barrier to the adoption of FEPbased techniques since, in some situations, computational techniques topredict protein-ligand binding free energies must take less wall clocktime than synthesizing the small molecule and experimentally testing thebinding affinity if these techniques are to have value, (e.g., in anindustrial drug design setting). This demand for speed has motivated abroad use of continuum theories of hydration within empirical scoringfunctions to describe the contributions of the solvent to the bindingaffinity of the complex. However, it is still an unsettled question asto whether or not these continuum solvation theories describe theunderlying molecular physics with sufficient accuracy to reliably rankthe binding affinities of a set of ligands for a given protein.

SUMMARY

Methods of calculating differences of binding affinities betweencongeneric pairs of ligands by way of a displaced solvent functional aredescribed.

Some embodiments include procedures for enumerating local statisticalthermodynamic properties of water solvating a receptor including (a)sampling configurations of the water solvating a receptor; (b)extracting thermodynamic information about the solvating water from theconfigurations including (i) automatically partitioning observed waterconfigurations into hydration sites, (ii) computing average systeminteraction energies of water molecules occupying the hydration sites,and (iii) computing excess entropies of the water molecules occupyingthe hydration sites, and (c) enumerating the local statisticalthermodynamic properties of water solvating the receptor. moleculardynamics simulations can be used to sample the configurations of thesolvating water in the receptor. Monte Carlo techniques can also be usedto sample the configurations of the solvating water in the receptor. Thewater configurations can automatically partitioned into hydration sitesby clustering the water configurations into regions of high wateroccupancy. Orientational contributions to the excess entropy can becomputed using a mixed quaternion/Euler angle technique. The receptorcan be an active site of a protein.

Some embodiments include procedures for computing a binding affinity ofa ligand for a receptor including (a) calculating local statisticalthermodynamic properties of water molecules solvating the receptor; and(b) calculating free energy gain of displacement of solvent from thereceptor by the ligand including (i) inserting the ligand into thereceptor, (ii) algorithmically determining steric overlap of the ligandwith the solvent, and (iii) calculating the free energy gain of theligand sterically displacing the solvent based on the results of thealgorithmically determining steric overlap of the ligand with thesolvent and the thermodynamic properties of the displaced solvent. Theprocedure can further include (c) sampling configurations of the watersolvating a receptor; (d) extracting thermodynamic information about thesolvating water from the configurations including (i) automaticallypartitioning observed water configurations into hydration sites, (ii)computing average system interaction energies of water moleculesoccupying the hydration sites, and (iii) computing excess entropies ofthe water molecules occupying the hydration sites. The free energy gainfor atoms of the ligand sterically displacing the solvent can becomputed as a function of the thermodynamic properties of the solvent.The free energy gain for atoms of the ligand sterically displacing thesolvent can be the excess chemical potential of the solvent as comparedto bulk fluid. Enthalpic contribution to the free energy of a ligandatom displacing a hydration site is assigned as a constant where a valueof the average system interaction energy exceeds a threshold. Entropiccontribution to the free energy of a ligand atom displacing a hydrationsite is assigned as a constant where a value of the excess entropyexceeds a specified threshold. The parameters of constant gains upondisplacement and threshold values are determined by optimizing againstexperimental data. The free energy gain is computed as the sum of (1)the output of the function of the thermodynamic properties of thesolvent and (2) the excess chemical potential of the solvent as comparedto bulk fluid.

Some embodiments include procedures where the free energy gain iscomputed as the sum of the enthalpic and entropic contribution of aligand atom displacing a hydration site. Values for a particularreceptor are optimized by using data for ligands binding to thatreceptor. Values for a class of receptors are optimized by fitting todata from a diverse set of receptor-ligand complexes. Some proceduresfurther include (c) calculating a difference in binding free energybetween two ligands, the difference corresponding to the difference inthe free energy gain from water displacement. The ligands can be acongeneric pair. The ligands differ by deletions of atoms.

Some embodiments include procedures for constructing a 3 dimensionalhydration thermodynamics map of a receptor including (a) calculatinglocal statistical thermodynamic properties of water molecules solvatingthe receptor; and (b) visualizing the local statistical thermodynamicproperties of the solvent as hydration sites against the backdrop of thereceptor. Other embodiments further include (e) sampling configurationsof the water solvating a receptor; and (d) extracting thermodynamicinformation about the solvating water from the configurations including(i) automatically partitioning observed water configurations intohydration sites, (ii) computing average system interaction energies ofwater molecules occupying the hydration sites, and (iii) computingexcess entropies of the water molecules occupying the hydration sites.The hydration sites can be displayed against the backdrop of thereceptor when the energetic and entropic properties are above or belowone or more cutoff values. A color code can be used to represent theenergetic and entropic properties of the displayed hydration sites.Other embodiments further include (e) visualizing a ligand in thereceptor superimposed with the hydration sites.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG.1 is a depiction according to some embodiments of the describedsubject matter and depicts system interaction energies (E) and theexcess entropic contributions to the free energy (−TS^(e)) of watermolecules in the principal hydration sites of the factor Xa active site.The system interaction energy includes the average energy of interactionof the water molecules in a given hydration site with the rest of thesystem and the excess entropic contribution to the free energy iscalculated from a truncated expansion of the excess entropy in terms ofcorrelation functions. Those hydration sites that were expected to makelarge energetic contributions when evacuated by the ligand are circledin gray, those expected to make large entropic contributions are circledin green, and those expected to make both entropic and enthalpiccontributions are circled in purple.

FIG. 2 is a depiction according to some embodiments of the describedsubject matter. Those hydration sites expected to contribute favorablyto binding when evacuated by the ligand are here shown within the factorXa active site in wire frame. Those expected to contribute energeticallyare shown in gray, those expected to contribute entropically are shownin green, and those expected to contribute energetically andentropically are shown in purple. The S1 and S4 pockets are labeled inyellow, as are several hydration sites discussed in the text.

FIG. 3 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns computed relative activitiesusing the 5-parameter form of equation 2 versus experimental relativeactivities of the 31 congeneric inhibitor pairs with factor Xa. Note thestability of this fit under leave-one-out cross validation.

FIG. 4 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns computed relative activitiesusing the 3-parameter form of equation 2 versus experimental relativeactivities of the 31 congeneric inhibitor pairs with factor Xa. Note thestability of this fit under leave-one-out cross validation.

FIG. 5 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns ligand 2J4I:38 (left) and ligand2J4I:GSJ (right) in the factor Xa active site. The hydration sites thatreceive an energetic score in equation 2 are depicted in gray wireframe, the hydration sites that receive an entropic score are depictedin green wire frame, and the hydration sites that receive both energeticand entropic scores are depicted in purple wire frame. Several hydrationsites discussed in the text are labeled in yellow. The experimentallymeasured affinity difference between these two compounds isΔΔG_(exp)=−6.26 kcal/mol. The optimized 3- and 5-parameter functionalspredicted ΔΔG_(3p)=−4.87 and ΔΔG_(5p)=−4.83 respectively. The isopropylgroup of ligand 2J4I:GSJ displaces three energetically depletedhydration sites, two of which are predicted to also be entropicallystructured, which resulted in a large predicted contribution to thebinding affinity of the complex.

FIG. 6 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns ligand 1MQ5:XLC (left) and ligand1MQ6:XLD (right) in the factor Xa active site. The hydration sites thatreceive an energetic score in equation 2 are depicted in gray wireframe, the hydration sites that receive an entropic score are depictedin green wire frame, and the hydration sites that receive both energeticand entropic scores are depicted in purple wire frame. Several hydrationsites discussed in the text are labeled in yellow. The experimentallymeasured affinity difference between these two compounds isΔΔG_(exp)=−2.94 kcal/mol. The optimized 3- and 5-parameter functionalspredicted ΔΔG_(3p)=−2.85 and ΔΔG_(5p)=−2.54 respectively. Unlike the S4group of ligand 1MQ5:XLC, the S4 pocket group of ligand 1MQ6:XLDdisplaced the energetically depleted and entropically structuredhydration site 13 and partially displaced entropically structuredhydration sites 20, which resulted in a large solvent relatedcontribution to the binding affinity quantitatively predicted.

FIG. 7 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns ligand 2BQ7:IID (left) and ligand2BQW:IIE (right) in the factor Xa active site. The hydration sites thatreceive an energetic score in equation 2 are depicted in gray wireframe, the hydration sites that receive an entropic score are depictedin green wire frame, and the hydration sites that receive both energeticand entropic scores are depicted in purple wire frame. Several hydrationsites discussed in the text are labeled in yellow. The experimentallymeasured affinity difference between these two compounds isΔΔG_(exp)=−2.01 kcal/mol. The optimized 3- and 5-parameter functionalspredicted ΔΔG_(3p)=−1.73 and ΔΔG_(5p)=−1.95 respectively. Unlike the S1group of ligand 2BQ7:IID, the S1 pocket group of ligand 2BQW:IIEdisplaces the energetically depleted and entropically structuredhydration site 12 found within the S1 subgroove. The contribution to thebinding affinity predicted by the 3-parameter and 5-parameterdisplaced-solvent functionals agreed with experiments.

FIG. 8 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns ligand 2BMG:25 (left) and ligand2BMG:I1H (right) in the factor Xa active site. The hydration sites thatreceive an energetic score in equation 2 are depicted in gray wireframe, the hydration sites that receive an entropic score are depictedin green wire frame, and the hydration sites that receive both energeticand entropic scores are depicted in purple wire frame. Several hydrationsites discussed in the text are labeled in yellow. The experimentallymeasured affinity difference between these two compounds isΔΔG_(exp)=−1.05 kcal/mol. The optimized 3- and 5-parameter functionalspredicted ΔΔG_(3p)=−1.31 and ΔΔG_(5p)=−1.31 respectively. The additionof a methoxyl group to ligand 2BMG:I1H displaces an energeticallydepleted hydration site from the linker region of the active site whichsolvates a disulfide bond between residues 191 and 220. Both functionalspredicted the contribution of displacing this hydration site to thebinding affinity of the complex with high accuracy.

FIG. 9 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns ligand 1V3X:D76 (left) and ligand1V3X:57 (right) in the factor Xa active site. The hydration sites thatreceive an energetic score in equation 2 are depicted in gray wireframe, the hydration sites that receive an entropic score are depictedin green wire frame, and the hydration sites that receive both energeticand entropic scores are depicted in purple wire frame. Several hydrationsites discussed in the text are labeled in yellow. The experimentallymeasured affinity difference between these two compounds isΔΔG_(exp)=−0.05 kcal/mol. The optimized 3- and 5-parameter functionalspredicted ΔΔG_(3p)=0.0 and ΔΔG_(5p)=0.0 respectively. The addition ofthe amide group to ligand D76 contribute negligibly to the bindingaffinity of the complex, which the technique predicted from the locationof the amide group away from any structured or energetically depletedhydration sites.

FIG. 10 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns ligand 1NFX:RDR (left) and ligand1NFU:RRR (right) in the factor Xa active site. The hydration sites thatreceive an energetic score in equation 2 are depicted in gray wireframe, the hydration sites that receive an entropic score are depictedin green wire frame, and the hydration sites that receive both energeticand entropic scores are depicted in purple wire frame. Several hydrationsites discussed in the text are labeled in yellow. The experimentallymeasured affinity difference between these two compounds is ΔΔGexp=−0.59kcal/mol. The optomized 3- and 5-parameter functionals predictedΔΔG_(3p)=+1.94 and ΔΔG_(5p)=+1.53 respectively. The poor agreement ofthe theory with experiment here is due to the Poor interaction energy ofthe S1 pocket sulfur atom of 1NFX:RDR with Ser195 compared withhydration 5, which is not displaced when ligand 1NFU:RRR docks with thereceptor.

FIG. 11 is a depiction according to some embodiments of the describedsubject Matter. The depiction concerns computed activities using the5-parameter form of equation 2Versus experimental activities for the setof 28 inhibitors with factor Xa. The poor stability of the Fit undercross validation suggested substantial over-fitting.

FIG. 12 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns computed activities using the5-parameter form of equation 2 versus experimental activities for theset of 28 inhibitors with factor Xa. The moderate stability of the fitunder cross validation suggested the problems associated withover-fitting were reduced when the three parameter form of equation 2was used.

FIG. 13 is a depiction according to some embodiments of the describedsubject matter. Those hydration sites expected to contribute favorablyto binding when evacuated by the ligand are here shown within the CDK2active site in wire frame. Those expected to contribute energeticallyare shown in gray, those expected to contribute entropically are shownin green, and those expected to contribute energetically andentropically are shown in purple. Those hydrations sites discussed inthe text are labeled in yellow.

FIG. 14 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns computed relative activitiesusing the 5-parameter form of equation 2 versus experimental relativeactivities of the 47 congeneric inhibitor pairs with CDK2.

FIG. 15 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns computed relative activitiesusing the 3-parameter form of equation 2 versus experimental relativeactivities of the 47 congeneric inhibitor pairs with CDK2.

FIG. 16 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns computed relative activitiesusing the 5-parameter form of equation 2 versus experimental relativeactivities of the 78 congeneric inhibitor pairs with Factor Xa and CDK2.

FIG. 17 is a depiction according to some embodiments of the describedsubject matter. The depiction concerns computed relative activitiesusing the 3-parameter form of equation 2 versus experimental relativeactivities of the 78 congeneric inhibitor pairs with Factor Xa and CDK2.

DETAILED DESCRIPTION

In one embodiment, a technique of directly computing the thermodynamicproperties of water molecules solvating the active site of theapoprotein is described. In this embodiment, techniques have beenapplied to understand the thermodynamics of ligand binding in factor Xa(fXa) and cyclin dependent kinase 2 (CDK2). FXa can be an important drugtarget in the thrombosis pathway and CDK2 can be an target for nextgeneration anticancer treatments. Techniques involve automaticallypartitioning the solvent density by way of a clustering technique tobuild a map of water occupancy in the protein active site, and assigningchemical potentials to the water sites using an expansion of the entropyin terms of correlation functions. Another embodiment includes a semiempirical extension of the model which enables computation of freeenergy differences (ΔΔG values) for selected pairs of fXa and CDK2ligands from merely a single explicitly solvated molecular dynamicssimulation of a ligand-free protein structure. Initial results suggestthat any protein structure, by which results can be examined, isacceptable as long as it is compatible with the congeneric ligand pairsbeing studied). In some embodiments, the free energy differencescalculated from the semi empirical model are shown to correlate wellwith experimental data (R²=0.81 for fXa and R²=0.53 for CDK2) via theuse of three adjustable parameters. In some example simulations, 31pairs of fXa ligands and 47 pairs of CDK2 ligands were investigatedusing data from a single 10 ns MD simulation of each receptor,illustrating the high computational efficiency of the describedtechniques. Furthermore, the solvent chemical potential map producedhere appears to elucidate features of the known fXa and CDK2 structureactivity relationships (SARs), and can provide a useful starting pointfor efforts to design novel compounds. An effort to calculate absolutebinding free energies for highly diverse ligands can display lessaccuracy and some over fitting (as would be expected, since thedisplacement of water molecules is not the single factor determiningbinding affinity), but still shows a significant correlation withexperimental data for this data set.

One embodiment provides a technique to exhaustively enumerate thethermodynamic properties of the water molecules solvating the activesite of a protein in its apostate and calculate the relative bindingaffinities of congeneric compounds that bind to this protein. Thistechnique includes (a) sampling the configurations of the solvatingwater in the active site with molecular dynamics simulation; (b)extracting the thermodynamic information about the solvating water fromthese configurations by (i) automatically partitioning the observedwater configurations by way of clustering the observed waterconfigurations into regions of high water occupancy (e.g., “hydrationsites”), (ii) computing the average system interaction energies of watermolecules occupying the various hydrations sites, (iii) computing excessentropies of the water molecules occupying the hydration sites; (c)constructing a 3-dimensional hydration thermodynamics map of the proteinactive site; and (d) computing the relative binding affinities ofcongeneric ligands based on the principle that tighter binding ligandswill displace more entropically structured and energetically depletedhydration sites from the active site into the bulk fluid.

The sampling of the configurations of the solvating water can beperformed by some technique that can reproduce the thermal ensemble ofthe water molecules hydrating the protein active site. In someembodiments, explicitly solvated molecular dynamics simulations of theprotein can be used to build this ensemble. In some embodiments,Metropolis Monte Carlo or replica exchange molecular dynamicssimulations can also be used.

In one embodiment, the extraction of the thermodynamic information ofthe solvating water is performed by a technique of applying an expansionof the excess entropy in terms of correlation functions to the activesite of a protein, where the water molecules can freely exchange withthe bulk fluid. Because the water molecules were free to exchange withthe bulk fluid, a rigorous definition of the active site volume wasconstructed. This allows unambiguous determination of when a watermolecule is within the active site and when it is not. This rigorousdefinition can be constructed, by way of example, according to thetechniques described in the experiments. This definition allowed theextraction of the coordinates and properties of all water moleculesfound in the active site of the protein during the simulation. Thisdistribution of water molecules was assumed to be the equilibriumdistribution and will be referred to, in some embodiments, as the activesite solvent density distribution.

Although the difficulty posed by waters exchanging with the bulk fluidis alleviated by definition of the active site, the inhomogenoustopography of the protein surface made the orientational distributionsof the water molecules dependent on their position within the activesite. Some embodiments include a procedure to partition the active sitevolume into small subvolumes, known in some embodiments as hydrationsites (the description of this term is not limited thereto), and treatedthe angular distributions as independent of position in thesesubvolumes. Identification of the subvolumes was accomplished byapplying a clustering algorithm to partition the solvent densitydistribution into a set of non-overlapping high water occupancy 1 Åradius spheres. This algorithm cycled through the positions of theoxygen atom of every water molecule found in the active site solventdensity distribution and found the position that has the greatest numberof water neighbors within a 1 Å radius. This position, in someembodiments, can be known as a hydration site and the hydration site canbe removed it and all of the oxygen positions within 1 Å of it from thesolvent density distribution. This process was then repeated, cyclingthrough the remaining positions. This loop was terminated when theclustering algorithm identified a hydration site with a water-oxygenoccupancy less than twice the expected value of a 1 Å radius sphere inthe bulk fluid. These hydration sites are defined subvolumes of theactive site and have good convergence properties for the expansion ofthe excess entropy in terms of correlation functions since they havesparse water density toward the edges of the clusters.

In some embodiments, it was defined that the system interaction energy(E_(hs)) of each hydration site is the average energy of interaction ofthe water molecules in a given hydration site with the rest of thesystem. This quantity was extracted from the molecular dynamicssimulations of the solvated aporeceptor.

The partial excess entropy (S^(e)) of each hydration site was alsocomputed by numerically integrating the inhomogeneous salvation theoryexpansion of the entropy in terms of orientational and spatialcorrelation functions. In this example, contributions from the firstorder term for each hydration site were included:

$\begin{matrix}{S^{e} = {{{- \frac{k_{b}\rho_{w}}{\Omega}}{\int{{g_{s\; w}\left( {r,\omega} \right)}{\ln\left( {g_{s\; w}\left( {r,\omega} \right)} \right)}{\mathbb{d}r}{\mathbb{d}\omega}}}} \approx {{{- k_{b}}\rho_{w}{\int{{g_{s\; w}(r)}{\ln\left( {g_{s\; w}(r)} \right)}{\mathbb{d}r}}}} - {\frac{k_{b}N_{W}^{V}}{\Omega}{\int{{g_{s\; w}(\omega)}{\ln\left( {g_{s\; w}(\omega)} \right)}{\mathbb{d}\omega}}}}}}} & (1)\end{matrix}$where r and ω are the Cartesian position and Euler angle orientation ofa water molecule, g_(sw)(r, ω) is the 1-body distribution of the water(w) at r and ω in the fixed reference frame of the solute protein (s),ρ_(w) is the density of bulk water, k_(b) is the Boltzmann constant, Ωis the total orientational space accessible to a water molecule, andN^(V) _(W) is total number of water oxygens found within a givenhydration site of volume V.

The computation of the translational one-body integral for eachhydration site was performed by discretizing the spherical coordinatespace of each hydration site into 0.03 A bins along r, 15° bins along Θ,and 30° bins along Φ.

The integration of the rotational component of the one-body termthroughout a subvolume V is performed semi analytically by way of amixed quaternion/Euler angle formalism. Each water oxygen within thecluster is translated to a common reference point and the hydrogens aremoved accordingly. The hydrogen to hydrogen mapping that allows for thesmallest rotation of a water in the cluster onto the coordinates of areference water are determined by a hydrogen to hydrogen distancecriterion, i.e., H₁->H_(a) and H₂->H_(b) should be minimal. Thequaternion maps H₁->H_(a) using the rotational axis orthogonal to theOH₁ and OH_(a) bond vectors. This rotation is applied and a secondquaternion is determined that will rotate H₂->H_(b) using the analogousorthogonal axis. These two quaternions are combined using the analyticalcombination rules to derive the single “master” quaternion that rotatesH₁->H_(a) and H₂->H_(b) simultaneously. It should be noted that thecalculation of this quaternion can be done in a single stage using theaxis of rotation orthogonal to vectors H₁H_(a) and H₂H_(b). From the“master” quaternion, the three Euler angles that rotate a cluster wateronto the reference water were analytically extracted. This process wasrepeated for all waters and the rotational correlation function wasdetermined from the distribution of the Euler angles and used tonumerically integrate the one-body rotational term using a 10°discretization.

The 3-dimension hydration thermodynamics map was constructed by plottingthe locations of the hydration sites in space relative to the surface ofthe protein and color coding the hydration sites to represent theirthermodynamic properties.

In some embodiments, computing the relative binding affinities ofcongeneric ligands from the information contained in this hydrationthermodynamics map was based on the following physical concepts: (1) ifa heavy atom of a ligand overlapped with a hydration site, it displacedthe water from that site; and (2) the less energetically or entropicallyfavorable the expelled water, the more favorable its contributions tothe binding free energy. A hydration site would contribute to thebinding free energy if its excess entropy or system interaction energywere beyond the fitted entropy and energy cutoff parameters S_(co) andE_(co), respectively. A flat reward was given for any hydration sitethat had excess entropies or system interaction energies that werebeyond these values. The amplitude of the reward values, S_(rwd) andE_(rwd), were fit accordingly. A fit cutoff distance (R_(co)) was usedto determine whether a heavy atom of the ligand displaced water from ahydration site. If the ligand heavy atom had the same position as thehydration site, the full value of S_(rwd) and E_(rwd) would be awarded.The reward was then linearly reduced to zero over the distance R_(co).This scoring function was implemented as

$\begin{matrix}{{\Delta\; G_{lig}} = {{\sum\limits_{{lig},{hs}}\;{{E_{rwd}\left( {1 - \frac{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}{R_{co}}} \right)}{\Theta\left( {E_{hs} - E_{co}} \right)}{\Theta\left( {R_{co} - {{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}}} \right)}}} - {T{\sum\limits_{{lig},{hs}}\;{{S_{rwd}\left( {1 - \frac{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}{R_{co}}} \right)}{\Theta\left( {S_{hs}^{e} - S_{co}} \right)}{\Theta\left( {R_{co} - {{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}}} \right)}}}}}} & (2)\end{matrix}$where ΔG_(bind) was the predicted binding free energy of the ligand,E_(hs) is the system interaction energy of a hydration site, S^(e) _(hs)is the excess entropy of a hydration site, and Θ is the Heaviside stepfunction. This implementation can be known, without limitation, to bethe “displaced-solvent functional”. Implementing this displaced-solventfunctional was particularly simple since it is merely a sum over theligand heavy atoms and a restricted sum over the entropically structuredand energetically depleted hydration sites, with a linear function ofthe hydration-site-ligand-atom approach distance as its argument. Notethat some hydration sites contributed in both the entropic and energeticsums. We also constructed a 3-parameter scoring function based on thesame principles as the 5-parameter scoring function, where the value ofR_(co) was set to 2.8 Å and the values of S_(rwd) and E_(rwd) wereforced to be equal. The parameterization of these terms can beaccomplished, by way of example, according to the techniques describedin the Experimental Details.

EXPERIMENTAL DETAILS

The present subject matter describes techniques to map the thermodynamicproperties of the water molecules solvating the active site of a proteinand techniques to compute the differences in binding affinity ofcongeneric ligands for the protein from this hydration thermodynamicsinformation. In some embodiments, the configurations of the solvatingwater in the active site are sampled with molecular dynamics simulation,and from this molecular dynamics trajectory the subject matter describesan algorithm to extract the thermodynamic properties of all regions inthe active site with high water occupancy. These high water occupancyregions can be referred to as, without limitation, “hydration sites.”Energetic and entropic information for water molecules occupying thesehydration sites are directly computed. In some embodiments, the subjectmatter includes a technique to display this hydration thermodynamicsinformation visually as a map of the local chemical potential of watermolecules solvating the active site. Some embodiments include analgorithm that can compute differences in binding affinity of congenericligands for the protein based on the physical principle that tighterbinding ligands will evacuate water from more entropically structuredand energetically depleted hydration sites.

In some embodiments, the present subject matter can be used to study thethermodynamics of ligand binding in factor Xa (fXa) and cyclin dependentkinase 2 (CDK2) (as shown by the example below). FXa can be an importantdrug target in the thrombosis pathway and CDK2 can be an attractivetarget for next generation anticancer treatments. This has led to activetargeting of both these systems by the pharmaceutical industry. The freeenergy differences calculated from the semi empirical model are shown tocorrelate with experimental data (R²=0.81 for FXa and R²=0.53 for CDK2)via the use of three adjustable parameters. 31 pairs of fXa ligands and47 pairs of CDK2 ligands were investigated using data from only a single10 ns MD simulation of each receptor, illustrating a high computationalefficiency of the techniques. Furthermore, the solvent chemicalpotential map produced here appears to elucidate features of the knownfXa and CDK2 structure activity relationships (SAR), and can provide auseful starting point for efforts to design novel compounds. An effortto calculate absolute binding free energies for highly diverse ligandsdisplays less accuracy and some over-fitting (as would be expected,since the displacement of water molecules is not the single factordetermining binding affinity), but still shows a significant correlationwith experimental data for this data set.

I. Application to the Test System Factor Xa

A. Structure Preparation and Simulation of Factor Xa

In some embodiments, PDB crystal structure 1FJS were chosen to be usedas the initial model of the fXa protein. This structure was importedinto an appropriate protein structure visualization program (e.g. theMaestro program), all crystallographic water was deleted, and hydrogenswere added to the structure assuming a pH 7 environment. Chain L of thecrystal structure was also deleted, since it contained no atoms within20 Å of the fXa active site. An appropriate molecular mechanics program(e.g. the protein preparation utility found in Maestro) was used to runa restrained minimization of the protein in the presence of the 1FJScrystal structure ligand. This removed bad steric contacts and improvedthe quality of the protein-protein and protein-ligand hydrogen bondingwithout large rearrangements of the protein heavy atoms. Using anappropriate molecular mechanics potential energy function (e.g. theOPLSAA-2001 potential), this model of the protein was imported into anappropriate molecular dynamics program (e.g. a modified version ofGROMACS containing the velocity version of the Verlet integrator,Andersen temperature controls, and Parrinello-Rahman pressure controls).The system was then solvated in an appropriate molecular mechanicsrepresentation of the water solvent (e.g. a cubic TIP4P water box whereeach boundary was greater than 10 Å away from the protein and added onechlorine ion to neutralize the system).

The energy of the system was minimized to relieve bad steric contactsbetween the protein and the water and the system was equilibrated for100 ps with appropriate molecular dynamics protocols (e.g. velocityversion of the Verlet integrator and Berendsen temperature and pressurecontrols at 298 K and 1 bar, where a frame of the system was saved every1 ps.) The molecular mechanics interactions were modeled withappropriate protocols (e.g. the Lennard-Jones interactions weretruncated at 9 Å, the electrostatic interactions were described exactlyfor pairs within 10 Å and by Particle-Mesh-Ewald for pairs outside ofthis radius, and all protein heavy atoms were harmonically restrainedwith spring constants of 1000 kJ/mol/nm). The final 10 ps ofequilibration data was used to seed 10 different 1ns molecular dynamicstrajectories with appropriate protocols (e.g. with the velocity versionof the Verlet integrator, Andersen temperature controls, andParrinello-Rahman pressure controls at 298 K and 1 bar). For thesesimulations the Lennard-Jones, electrostatic forces, and harmonicrestraints on the heavy atoms of the protein were the same as in theequilibration simulations. Frames of this simulation were saved everypicosecond.

B. Active Site Hydration Analysis of Factor Xa

In order to analyze the thermodynamic and structural properties of thewater molecules hydrating the fXa active site, some sensible definitionwas developed for when a solvating water should be considered within thefXa active site and when it should not. A set of 35 fXa crystalstructures with bound inhibitors were used to define the volume of theactive site (PDB structures 1EZQ, 1F0R, 1F0S, 1FAX, 1FJS, 1G2L, 1G2M,1IOE, 1IQE, 1IQF, 1IQG, 1IQH, 1IQI, 1IQJ, 1IQK, 1IQL, 1IQM, 1IQN, 1KSN,1KYE, 1MQ5, 1MQ6, 1NFU, 1NFW, 1NFX, 1NFY, 1V3X, 1XKA, 1XKB, 2BOK, 2CJI,2J2U, 2J34, 2J38, 2J4I). A multiple structure alignment was computedbetween the 35 fXa crystal structures containing inhibitors and theprepared fXa model structure. This alignment rotated the crystalstructures onto the prepared fXa structure. This procedure also rotatedthe inhibitors found in these crystal structures into the active site ofthe prepared model fXa structure. The results of these alignments werehand inspected for severe steric clashes and none were found. Using thisset of aligned structures, the active site was designated as the volumecontaining all points in space that are within 3 Å of any ligand heavyatom. The position of the active site volume was constant throughout thesimulation because the protein heavy atoms were harmonically restrained.The coordinates of all waters observed within this region of spaceduring the 10 ns of simulation data were saved every picosecond. Thiswater distribution was considered to be the equilibrium distribution ofwater within the fXa active site and its thermodynamic properties werecharacterized as in the techniques described above.

Several measures of local water structure properties were alsocalculated for the water molecules found within each hydration site.These were the average number of water neighbors, the average number ofhydrogen bonding water neighbors, the fraction of the water neighborsthat were hydrogen bonding, and the water exposure of each hydrationsite. These averages are for all water molecules in each hydration site.The number of neighbors value is the average number of water moleculesfound within 3.5 Å, where the distance is measured water oxygen to wateroxygen. A geometric definition of a hydrogen bond was used where twowater molecules were deemed to be hydrogen bonded if their oxygens werewithin 3.5 Å of each other and at least one oxygen-oxygen-hydrogen anglewas less than 30°. The exposure value quantifies to what degree ahydration site is surrounded by other water molecules: a value of unitysuggests it is in a water environment similar to the bulk fluid, and avalue of zero suggests the hydration site is occluded from any othersolvent molecules. The exposure value is computed as the average numberof neighbors that water molecules in a hydration site have divided bythe average number of neighbors that a water molecule has in the bulk.

C. Construction of the Factor Xa Ligand Binding Affinity Data Sets

Within the PDB, 28 crystal structures of fXa were found bound to variousinhibitors with thermodynamic binding data (2BOK, 2J2U, 2BQ7, 1G2L,2J38, 1G2M, 1KYE, 1F0R, 1F0S, 2BMG, 1NFU, 2J34, 1LQD, 2CJI, 2BQW, 1NFX,2BOH, 1NFY, 1NFW, 1MQ5, 2J4I, 1EZQ, 1KSN, 1Z6E, 2G00, 1FJS, 2FZZ, 1MQ6).A multiple structure alignment was computed between the 28 fXa crystalstructures containing inhibitors and the prepared fXa model structure.This procedure rotated the 28 inhibitors found in these crystalstructures into the active site of the prepared model fXa structure. Theresults of these alignments were hand inspected for severe stericclashes and none were found. The orientations of each of these 28inhibitors with respect to the prepared model fXa structure were savedand were referred to as the 28 crystal structure ligand set.

From this set of 28 crystal structure ligand set, a set of 31 congenericinhibitor pairs was prepared. The goal of this set of inhibitor pairswas to isolate the effects of solvent displacement on the free energy ofbinding. Each congeneric pair was created by either noting that 2 of thecrystal structure ligands reported in the prior set were congeneric orby building a congeneric pair from a single crystal structure ligand bydeleting or swapping atoms of the crystal structure ligand. Severalrules were devised to construct this set. When any two members of the 28crystal structure ligand set were reported in the same publication anddiffered by no more than 3 chemical groups, they were consideredcongeneric pairs. When the publication reporting the crystal structureligand contained congeneric series data for structurally similarligands, Three rules were followed to build new congeneric pairs:

-   -   1. Atoms were deleted from a crystal structure ligand, and they        were not added.    -   2. Deletions of atoms that resulted in a group that could rotate        around a single bond and donate hydrogen bonds were eliminated    -   3. A congeneric pair that was built by changing the identity of        a ligand atom (for instance, by changing a carbon atom to an        oxygen atom) can be required to have the change applied to both        members of the pair.

These three rules were intended to minimize the error of assuming thatthe binding mode of the new inhibitor structures, which were built fromdeleting and swapping atoms of the crystallized inhibitors, would notchange. These rules were also intended to minimize differences incontributions to binding affinity from non-solvent related terms foreach inhibitor pair, such as the loss of entropy of docking the ligand,the strength of the interaction energy between the ligand and theprotein, and the reorganization free energy of the protein. Excludedsolvent density effects were expected to dominate this set since theseother non-solvent related terms contributing to the free energy ofbinding would be relatively constant for each congeneric pair. It wasalso chosen to limit comparison of binding affinities between pairs ofligands that were determined in the same publication, due to thevariance in experimental techniques commonly employed. The resulting setcan be referred to as the set of 31 congeneric inhibitor pairs.

D. Development and Parameterization of the Displaced-Solvent Functionalfor Factor Xa

A 5-parameter scoring function was devised to determine if the relativebinding affinities of the 28 crystal structure ligands and the bindingaffinity differences of the 31 congeneric inhibitor pairs correlatedwith the thermodynamic properties of the displaced active site solvent.The form of the functional was a sum over ligand heavy atoms and a sumover hydration sites. Each time a ligand heavy atom was found withinsome parameterized distance of a hydration site with an interactionenergy or excess entropy predicted to be favorable to evacuate by somefit empirical criteria, an additive contribution was summed. Thefunctional itself was

$\begin{matrix}{{\Delta\; G_{lig}} = {{\sum\limits_{{lig},{hs}}\;{{E_{rwd}\left( {1 - \frac{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}{R_{co}}} \right)}{\Theta\left( {E_{hs} - E_{co}} \right)}{\Theta\left( {R_{co} - {{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}}} \right)}}} - {T{\sum\limits_{{lig},{hs}}\;{{S_{rwd}\left( {1 - \frac{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}{R_{co}}} \right)}{\Theta\left( {S_{hs}^{e} - S_{co}} \right)}{\Theta\left( {R_{co} - {{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}}} \right)}}}}}} & (3)\end{matrix}$where ΔG_(bind) was the predicted binding free energy of the ligand,R_(co) was the distance cutoff for a ligand atom beginning to displace ahydration site, E_(co) was the minimum E_(hs) of a hydration site thatwas considered energetically depleted, E_(rwd) was the energeticcontribution to ΔG_(bind) for displacing an energetically depletedhydration site, S_(co) was the minimum S^(c) term of a hydration sitethat was considered entropically structured, −TS_(rwd) was the Entropiccontribution to ΔG_(bind) for displacing an entropically structuredhydration site, and Θ was the Heaviside step function. A 3-parameterform of this equation was also considered where R_(co)=2.8 Å and−TS_(rwd)=E_(rwd were fixed).

The parameters were optimized by a Monte Carlo walk in parameter space.The error-function used to train the parameters was theroot-mean-square-deviation of the predicted relative binding freeenergies of the 28 crystal ligands and the root-mean-square-deviation ofthe differences in the binding free energies of the 31 congeneric pairs.For the training of the 3- and 5-parameter functionals on the 28 crystalstructure ligand set, an initial seed value of R_(co)=2.8 Å was chosen,along with E_(rwd)=−0.5 kcal/mol, −TS_(rwd)=−0.5 kcal/mol, E_(co)=−18.5kcal/mol, and TS_(co)=1.5 kcal/mol. Five separate 1000 stepoptimizations were run where the first move was always accepted and thelowest RMSD value encountered in these optimizations was taken to be theoptimal parameter set. The initial seed values used to train the 3- and5-parameter functionals on the set of 31 congeneric inhibitor pairs wereR_(co)=2.8 Å, E_(rwd)=−1.0 kcal/mol, −TS_(rwd)=−1.0 kcal/mol,E_(co)=−18.5 kcal/mol, and TS_(co)=1.5 kcal/mol. The parameters werethen optimized in a procedure similar to that used for the 28 crystalstructure ligands.

The error of the resulting optimized functionals were estimated withleave-one-out cross validation. In this technique a functional istrained to an N−1 point subset of data and then the value of point N ispredicted with this functional. This is repeated N times, once for eachdata point, and the error of the functional is estimated by summing theerror of the predictions for each of these points. The Pearsoncorrelation coefficient (R²) computed in this procedure for the N datapoints is bounded between the R² value found by training of thefunctional on all N data points and zero. A cross validation R² valueclose to the R² value found by training of the functional on all N datapoints suggests very little over-fitting has occurred when training thefunctional.

II. Application to the Test System Cyclin Dependent Kinase 2

A. Structure Preparation and Simulation of Cyclin Dependent Kinase 2

In some embodiments, PDB crystal structure 1PKD was chosen to be used asthe initial model of the CDK2 protein. This structure was imported intoan appropriate protein structure visualization program (e.g. the Maestroprogram), all crystallographic water were deleted, and hydrogens wereadded to the structure assuming a pH 7 environment. A appropriatemolecular mechanics program (e.g. the protein preparation utility foundin Maestro) was then used to run a restrained minimization of theprotein in the presence of the 1PKD crystal structure ligand. Thisremoved bad steric contacts and improved the quality of theprotein-protein and protein-ligand hydrogen bonding without largerearrangements of the protein heavy atoms. Using an appropriatemolecular mechanics potential (e.g. the OPLSAA-2001 potential), thismodel of the protein was imported into an appropriate molecular dynamicsprogram (e.g. a modified version of GROMACS containing the velocityversion of the Verlet integrator, Andersen temperature controls, andParrinello-Rahman pressure controls). The system was then solvated in anappropriate molecular mechanics representation of the water solventcubic (e.g. a TIP4P water box where each boundary was greater than 10 Åaway from the protein).

The energy of the system was minimized to relieve bad steric contactsbetween the protein and the water and equilibrated 20 replicas of thesystem for 100 ps with appropriate molecular dynamics protocols (e.g.the Leapfrog integrator and Berendsen temperature and pressure controlsat 298 K and 1 bar, where a frame of the system was saved every 1 ps).The molecular mechanics interactions were modeled with appropriateprotocols (e.g. the Lennard-Jones interactions were truncated at 9 Å,the electrostatic interactions were described exactly for pairs within10 Å and by Particle-Mesh-Ewald for pairs outside of this radius, andall protein heavy atoms were harmonically restrained with springconstants of 1000 kJ/mol/nm). This equilibration data was used to seed20 different 500 ps molecular dynamics trajectories with appropriateprotocols (e.g. the Leapfrog integrator under NVE conditions). For thesesimulations the Lennard-Jones, electrostatic forces, and harmonicrestraints on the heavy atoms of the protein were similar to theequilibration simulations. Frames of this simulation were saved everypicosecond.

B. Active Site Hydration Analysis of Cyclin Dependent Kinase 2

In order to analyze the thermodynamic and structural properties of thewater molecules hydrating the CDK2 active site, some sensible definitionwas constructed for when a solvating water should be considered withinthe CDK2 active site and when it should not. A set of 42 CDK2 crystalstructures were used with bound inhibitors to define the volume of theactive site (PDB structures 1H1P, 1H1S, 1KE5, 1KE6, 1KE7, 1KE8, 1KE9,1OGU, 1OI9, 1OIQ, 1OIR, 1OIU, 1OIY, 1P2A, 1PXI, 1PXJ, 1PXK, 1PXL, 1PXM,1PXN, 1PXP, 1VYW, 1VYZ, 1Y8Y, 1Y91, 2B52, 2B53, 2B54, 2BHE, 2BKZ, 2C5P,2C5V, 2C5X, 2C68, 2C69, 2C6I, 2C6K, 2C6L, 2C6T, 2CLX, 2DUV, and 2FVD). Amultiple structure alignment was computed between the CDK2 crystalstructures containing inhibitors and the prepared CDK2 model structuremerely using residues Glu9-Glu13, Gly17-Ala22, Val30-Ile-36,Leu79-Lys90, Pro131-Asn137, and Leu144-Phe147. The structure alignmentused this subset of the protein sequence because the backbone geometryof the protein appeared to be roughly invariant along these portions ofthe sequence crystal structure to crystal structure. This alignmentrotated the crystal structures onto the prepared CDK2 structure, whichalso rotated the inhibitors found in these crystal structures into theactive site of the prepared model CDK2 structure. The results of thesealignments were hand inspected for severe steric clashes and none werefound. Using this set of aligned structures, the active site wasdesignated as the volume containing all points in space that are within3 Å of any ligand heavy atom. The position of the active site volume wasconstant throughout the simulation because the protein heavy atoms wereharmonically restrained. The coordinates of all waters observed withinthis region of space during the 10 ns of simulation data were savedevery picosecond. This water distribution was considered to be theequilibrium distribution of water within the CDK2 active site and itsthermodynamic properties were characterized according to the techniquesdescribed in the detailed description. Several measures of local waterstructure properties were also calculated for the water molecules foundwithin each hydration site, as was done for CDK2.

C. Construction of the Cyclin Dependent Kinase 2 Ligand Binding AffinityData Sets

From the set of CDK2 crystal structures aligned with the model 1PKDstructure, a set of 47 congeneric inhibitor pairs was prepared. The goalof this set of inhibitor pairs was to isolate the effects of solventdisplacement on the free energy of binding. Each congeneric pair wascreated by either noting that 2 of the crystal structure ligandsreported in the prior set were congeneric or by building a congenericpair from a single crystal structure ligand by deleting or swappingatoms of the crystal structure ligand. Several rules were devised toconstruct this set. When any two members of the CDK2 crystal structureligand set were reported in the same publication, differed by no morethan 3 chemical groups, and merely differed by deletions of atoms; theythen were considered congeneric pairs. When the publication reportingthe crystal structure ligand contained congeneric series data forstructurally similar ligands, three rules were followed to build newcongeneric pairs:

-   -   1. Atoms were deleted from a crystal structure ligand, and they        were not added.    -   2. Deletions of atoms that resulted in a group that could rotate        around a single bond and donate hydrogen bonds were eliminated    -   3. A congeneric pair that was built by changing the identity of        a ligand atom (for instance, by changing a carbon atom to an        oxygen atom) can be required to have the change applied to both        members of the pair.

These three rules were intended to minimize the error of assuming thatthe binding mode of the new inhibitor structures, which were built fromdeleting and swapping atoms of the crystallized inhibitors, would notchange. These rules were also intended to minimize differences incontributions to binding affinity from non-solvent related terms foreach inhibitor pair, such as the loss of entropy of docking the ligand,the strength of the interaction energy between the ligand and theprotein, and the reorganization free energy of the protein. Excludedsolvent density effects were expected to dominate this set since theseother non-solvent related terms contributing to the free energy ofbinding would be relatively constant for each congeneric pair. It waschosen to compare binding affinities between pairs of ligands that weredetermined in the same publication and in complex with either Cyclin Aor Cyclin E, due to the variance in experimental techniques commonlyemployed. The resulting set was designated as the set of 47 CDK2congeneric inhibitor pairs.

D. Development and Parameterization of the Displaced-Solvent Functionalfor Cyclin Dependent Kinase 2

The 5 parameter and 3 parameter forms of the displaced solventfunctional were trained to the set of 47 congeneric inhibitor pairs ofCDK2 through techniques similar to those used to train the functionalsfor fXa. The functional was also trained to a combined set of 31congeneric inhibitor pairs of fXa and 47 congeneric inhibitor pairs ofCDK2. The error in these parameter fittings were also estimated withleave-one-out cross validation.

III. Results

A. Mapping of the Thermodynamic Properties of the Active Site Solvent ofFactor Xa

The data for each fXa hydration site is presented in Table 1. FIG.1shows the calculated energies and excess entropies for each of thehydration sites in the fXa binding cavity. Relative to other hydrationsites, the hydration sites circled in gray had poor system interactionenergies, the hydration sites circled in green had unfavorable excessentropies, and the hydration sites circled in purple had both relativelypoor system interaction energies and entropies. FIG. 2 shows theresulting 3-dimensional active site hydration map with this same colorcoding.

The hydration site map generated by the described subject matter for thetest system fXa depicted in FIG. 2 elucidated several features of theexperimentally known SAR of the fXa ligands. Factor Xa inhibitorsgenerally bind in an L-shaped conformation, where one group of theligand occupies the anionic S1 pocket lined by residues Asp189, Ser195,and Tyr228 and another group of the ligand occupies the aromatic S4pocket lined by residues Tyr99, Phe174, and Trp215. Typically, a fairlyrigid linker group will bridge these two interaction sites. The solventanalysis identified three enthalpically unfavorable hydration sites,i.e., sites 13, 18, and 21, solvating the fXa S4 pocket. This findingagreed with the experimental result that the S4 pocket has anexceptionally high affinity for hydrophobic groups. A high excesschemical potential hydration site was identified, site 12, solvatingTyr228 in the S1 pocket. Several research groups have found thatintroducing a ligand chlorine atom at this location, and hencedisplacing the water from this site, makes a large favorablecontribution to the binding affinity. Additionally, an energeticallydepleted hydration site was identified, site 17, solvating the disulfidebridge between Cys191 and Cys220. Displacement of water from this sitewas expected to make favorable contributions to the binding free energy.This agrees with several reported chemical series targeting this site.

This hydration map was compared with the locations of active sitecrystallographic waters from the fXa apo-structure, crystal structure1HCG. Of the 11 crystallographic waters that resolve within the fXaactive site, 9 of these crystallographic waters are within 1.5 Å of ahydration site, and all of the crystallographic waters are within 2.5 Åof a hydration site. More hydration sites were identified in the activesite than crystallographically resolved waters. However, thisdiscrepancy is expected since the 1HCG crystal structure was merelysolved to a resolution of 2.2 Å, and it has been noted that the numberof crystallographic water molecules identified in X-ray crystallographyof proteins is quite sensitive to resolution (an average of 1.0 crystalwaters per protein residue is expected at a resolution of 2 Å, but anaverage of 1.6-1.7 crystal waters per residue is expected at aresolution of 1 Å). The number and location of crystallographic watersidentified in X-ray crystallography of proteins has also been found tobe sensitive to temperature, pH, solvent conditions, and the crystalpacking configuration. Given these sources of noise, the agreement wasfound to be satisfactory and in line with other similar comparisons ofthe solvent distributions obtained from molecular dynamics simulationswith those obtained from X-ray crystallography.

B. Development and Testing of the Displaced-Solvent Functional on theSet of the Factor Xa Congeneric Inhibitor Pairs

A dataset of 31 congeneric inhibitor pairs of fXa (see section I.C)(Table 2) was prepared. These 31 congeneric inhibitor pairs were pairsof fXa ligands that differed by at most three chemical groups. It wasexpected that excluded solvent density effects would dominate thisdataset since the other terms—the protein reorganization free energy,ligand conformational entropy, etc.—would be largely a consequence ofthe ligand scaffold shared by both members of the pair. The parametersof the displaced-solvent functionals were optimized to reproduce theexperimentally measured differences in binding affinity between each ofthese congeneric ligand pairs. The error of the resulting functionalswere estimated with leave-one-out cross validation. The resulting valuesof the parameters can be found in Table 3 and plots of the predicteddifferences in binding free energy versus the experimental values areshown in FIGS. 3 and 4. The agreement of the predictions of thefunctionals with the experimental data was notable: the Pearsoncorrelation coefficient (R²) was 0.81 for both the 3-parameter and5-parameter functionals. Under leave-one-out cross-validation, the R²value degraded to 0.80 and 0.75, respectively. From the good numericalagreement observed over the 6 kcal/mol free energy range ofmodifications plotted in FIGS. 3 and 4, this technique was welldifferentiated from modifications that make large contributions to thebinding affinity from modifications that merely make small modificationsto the binding affinity for this fXa test system. The predictive abilityof the displaced solvent functional on this series confirms that theeffect on the binding free energy of small complementary chemicalmodifications to existing leads can largely be understood by an analysisof the molecular properties of the solvent alone. Several congenericligand pairs were instructive in clarifying the particular strengths ofthis approach.

Congeneric ligands 2J4I:38 and 2J4I:GSJ, depicted in FIG. 5, wererepresentative of the types of modifications that would contribute wellto the binding affinity. These ligands differ in that GSJ has anadditional isopropyl group located in the S4 pocket. This isopropylgroup fills a portion of the S4 pocket that is lined by the side chainsof residues Tyr99, Phe174, and Trp215 and, in the absence of the ligand,is principally solvated by hydration sites 13, 18, and 21. Hydrationsite 13 is in close contact (<4.5 Å) with each of these three aromaticside chains and has a very low exposure parameter of 0.53. Watermolecules in this hydration site cannot form hydrogen bonds with thehydrophobic protein and maintain an average of 2.05 water-water hydrogenbonds, which leads to relatively unfavorable system interactionenergies. The hydrogen bonds that it does form are mainly donated byhydration sites 18 and 21 and very rarely by hydration site 1. Theorientational and translational restrictions necessary to maintain thishydrogen bonding profile result in relatively unfavorable excessentropies for water at this hydration site. The hydrophobic enclosurefor hydration sites 18 and 21 is not as tight (exposure parameters of0.66 and 0.74, respectively); however, the environment is otherwisequalitatively similar. Both these hydration sites have above-averagesystem interaction energies due to the hydrophobic bulk of the proteinenclosing them, and hydration site 18 was also identified by theempirical criteria of the described subject matter to be entropicallyunfavorable, although it was a borderline case. GSJ's additionalisopropyl group expels water from all three of the above-describedhydration sites: hydration sites 13 and 18 were predicted by theoptimized displaced-solvent functionals to make both energetic andentropic contributions to binding, and hydration site 21 was predictedto merely make energetic contributions. The experimentally measuredaffinity difference between these two compounds is ΔΔG_(exp)=−6.26kcal/mol. The optimized 3- and 5-parameter functionals predictedΔΔG_(3p)=−4.87 and ΔΔG_(5p)=−4.83, respectively. This agreed with theexperimental finding that adding ansopropyl group to ligand 2J4I:38 atthis location makes a large and favorable contribution to the bindingfree energy. The congeneric ligand pair 2J4I:32/2J4I:33 (ΔΔG_(exp)=−4.11kcal/mol) has precisely the same hydrogen/isopropyl substitution as the2J4I:38/2J4I:GSJ pair, and therefore the same values for ΔΔG_(3p) andΔΔG_(5p) of −4.87 and −4.83 kcal/mol, respectively, which matches verywell with ΔΔG_(exp).

The congeneric ligands 1MQ5:XLC and 1MQ6:XLD are depicted in FIG. 6.This pair has a more subtle modification of the group binding the S4pocket than the 2J4I:38-2J4I:GSJ congeneric pair described above. Forthis pair, the S4 binding group found in ligand 1MQ6:XLD overlapped withhydration sites 13 and 20, whereas the S4 binding group of ligand1MQ5:XLC did not. As noted above, expulsion of water from hydration site13 is expected to make both favorable energetic and entropiccontributions to binding. Water in hydration site 20 has favorableenergetic interactions due to several well-formed hydrogen bonds: watermolecules occupying this site predominately donate a hydrogen bond tothe backbone carbonyl group of Glu97, nearly always receive a hydrogenbond from hydration site 4, and have good hydrogen bonding interactionswith hydration site 35. Hydration site 20, though, also incurredunfavorable contributions to its excess entropy due to the structuringrequired to maintain these favorable interactions. When displaced by theS4 binding group of ligand 1MQ6:XLD, an electropositive carbon (thecarbon is bound to an oxygen) comes into close contact with the backbonecarbonyl group of Glu97. This electropositive carbon likely recapturesmuch of the interaction energy between the protein carbonyl group andthe water in hydration site 20 without the associated entropic cost.From these water thermodynamics considerations, the optimized 3- and5-parameter displaced-solvent functionals predict affinity differencesof ΔΔG_(3p)=−2.85 and ΔΔG_(5p)=−2.54, respectively. The experimentaldifference binding affinity between the two ligands is ΔΔG_(exp)=−2.94kcal/mol.

The congeneric ligands 2BQ7:IID and 2BQW:IIE are depicted in FIG. 7.This congeneric pair isolates the contribution of inserting a ligandchlorine atom into the region of the S1 pocket lined by the side chainsof residues Ala190, Val213, and Tyr228. The chlorine atom on 2BQW:IIEdisplaces water from hydration site 12, which is tightly enclosed by theside chains of residues Ala190, Val213, and Tyr228. The exposureparameter of this hydration site is merely 0.32. This tight enclosure byhydrophobic groups caused the system interaction energy of water in thishydration site to be several kcal/mol less favorable than in the neatfluid. Water molecules in this site maintained hydrogen bonds with itsfew water neighbors 92% of the simulation time, which made unfavorablecontributions to its excess entropy. The location of this hydration sitecoincided with the location of a structurally conserved water moleculethat several studies have shown is favorable to displace. Theexperimentally measured affinity difference between these two compoundsis ΔΔG_(exp)=−2.01 kcal/mol, whereas the optimized 3-and 5-parameterfunctionals predicted are ΔΔG_(3p)=−1.73 and ΔΔG_(5p)=−1.95,respectively.

The congeneric ligands 2BMG:25 and 2BMG:I1H are depicted in FIG. 8.These ligands differ in that ligand 2BMG:I1H has an additional methoxygroup that displaced water from hydration site 17, which solvates adisulfide bond between Cys191 and Cys220. Predicting the favorability ofadding a methoxy group at this position is highly nontrivial. Theportion of the active site solvated by hydration site 17 did not appearespecially hydrophobic (the side chains of Gln192 and Arg144 and thebackbone carbonyl group of Glu147 are within 4 Å of its position);however, due to the concave topography (exposure parameter of 0.48) ofthe protein surface, water molecules in this hydration site are unableto form as energetically favorable hydrogen bonding interactions withthe protein as they would with water in the bulk. This resulted in thesystem interaction energy of hydration site 17 to be ˜3 kcal/mol lessfavorable than water molecules in the bulk fluid. The size scale ofindividual water molecules is important here, since a field ofinfinitesimal dipoles would easily solvate both polar side chains andthe surrounding dipolar fluid. The two displaced-solvent functionalseach predict the difference in the binding affinity of ligands 2BMG:25and 2BMG:I1H within 0.26 kcal/mol of the experimentally measured value.

The congeneric ligands 1V3X:D76 and 1V3X:57 are depicted in FIG. 9.Ligand 1V3X:57 has an additional amide group which is oriented away fromthe protein in the linker region of the complex. The displaced-solventfunctionals correctly predicted that the addition of this group has amarginal contribution to the binding affinity. This is because the amidegroup does not displace water from any contributing hydration site. Itis interesting to note that the size of this added group isapproximately equal to that of the isopropyl group added in ligand pair2J4I:38-2J4I:GSJ. This underscored that the displaced-solvent functionalevaluated a weighted shape complementarity—i.e., it rewarded theintroduction of complementary groups where predicted to make largecontributions from the solvent properties and does not reward shapecomplementarity away from these regions. The experimentally measuredaffinity difference between these two compounds is ΔΔG_(exp)=−0.05kcal/mol. The optimized 3- and 5-parameter functionals both predict noaffinity difference between the two compounds, consistent with theexperimental ΔΔG.

Congeneric ligands 1NFX:RDR and 1NFW:RRR are depicted in FIG. 10. Theseligands differ by a substantial modification to the ring that binds theS1 pocket. They also differ by the removal of an ethanol group that isdistant from any contributing hydration sites. The S1 binding group ofligand 1NFX:RDR has a sulfur atom in close contact with Ser195. Thissulfur atom displaces water from hydration site 5, whereas ligand1NFW:RRR does not displace water from this site. Water molecules in thishydration site have favorable interactions with the protein and thesurrounding waters but are entropically structured. The structuring andcorresponding entropic penalties come from the large degree of enclosure(exposure parameter of 0.5) in combination with the energetic demands ofmaintaining favorable hydrogen bonding interactions with the protein andsurrounding water; notably, a persistent hydrogen bond is donated fromSer195 to the water molecules in this site. The displacement of waterleads the optimized 3- and 5-parameter functionals to predictΔΔG_(3p)=+1.94 and ΔΔG_(5p)=+1.53, respectively. However, theexperimentally measured difference in binding affinities isΔG_(exp)=−0.59 kcal/mol. The scoring function performed less desirablyfor this inhibitor pair because the sulfur atom in the benzothiophenegroup of ligand 1NFX:RDR and Ser195 questioned that the added chemicalgroups must be complementary to the protein surface. Thus, though thedisplacement of water from hydration site 5 should contribute favorablyto the binding free energy, it is more than offset by the loss ofhydrogen bonding energy between the water and Ser195. This resulted inthe displaced-solvent functional predicting 1NFX:RDR would be thetighter binding ligand, in disagreement with the experimental data.

C. Development and Testing of the Displaced-Solvent Functional on theSet of 28 Factor Xa Crystal Structure Ligands

In addition to the set of 31 congeneric pairs, a dataset of 28inhibitors was prepared taken from solved fXa crystal structures (seesubsection C) (Table 4). These fXa ligands belonged to many differentcongeneric series, and typically did not share a common chemicalscaffold with each other. In the previous section it was hypothesizedthe contributions to the free energy of binding from changes inconformational entropy, protein-ligand interaction energy, and proteinreorganization free energy would be similar for ligand pairs that shareda common chemical scaffold. If this was the case, the differences in thebinding free energies of congeneric pairs could be understood mainly byan analysis of the displaced solvent alone. The success of the 3- and5-parameter displaced-solvent functional outlined in the previoussection supports the validity of this hypothesis. However, for ligandpairs that do not share a common scaffold, it would be expected thatdifferences in these contributions would not be small and thatpredictions based solely on an analysis of the solvent would be lesssuccessful. Despite this concern, since the functional performed wellover the set of congeneric pair, it was desirable to determine how muchof the binding affinities of these ligands could be understood frommerely the contributions described by the displaced-solvent functional,as measured by the RMSD, absolute average error, and R² values. To studythis question, the 3- and 5-parameter displaced-solvent functionals wereoptimized to reproduce the experimentally measured differences inbinding affinities between 378 unique ligand pairs (all combinations) ofthis 28 ligand set, and leave-one-out (LOO) cross validation wasperformed to better estimate the error of the functionals. Values of theparameters can be found in Table 5 and the agreement of the fitfunctionals with the experimental data can be found in FIGS. 11 and 12.Although the 3- and 5-parameter functionals could be tuned to correlatereasonably well with the experimental data (R² of 0.50 and 0.48,respectively), the performance under leave-one-out cross validationsuggested substantial over-fitting of the 5-parameter functional (LOOR²=0.11). Notably, though, the cross validated R² of 0.30 for the3-parameter fit indicated terms of the type described by thedisplaced-solvent functional are important to understanding the absolutebinding thermodynamics of fXa ligand, but also clearly indicated thatmore traditional terms can also be helpful to quantitatively predictabsolute binding free energies with desired accuracies.

In both the 3- and 5-parameter fits of the displaced-solvent functionalto the set of 28 crystal structure ligands, two particular ligands,1MQ6:XLD and 1FJS:Z34, were consistently the worst outliers in the set.Both of these ligands have excellent overlap with contributing hydrationsites, but were out-scored by ligands that placed larger aromatic groupsat similar positions in the binding pocket, such as ligands 1Z6E:IK8,2FZZ:4QC, and 2G00:5QC. This error was expected because the pair-wisereward of atoms in close contact with the hydration sites approximatedto what degree the contributing hydration sites were displaced by thesurface of the ligand. Thus, when an aromatic group displaced ahydration site, a disproportionately large number of ligand atomscontributed in the displaced-solvent functional since the tightercovalent bonding in these groups placed many ligand atoms closer inspace to the hydration site than could be seen otherwise. When ligands1MQ6:XLD and 1FJS:Z34 were excluded from the fit, the leave-one-outcross validation of the 3- and 5-parameter functionals yielded R² valuesof 0.40 and 0.55, respectively. This dramatic improvement of thestability and quality of the fit underscores how poor the linearpair-wise approximation of the excluded volume of the ligand was forinhibitors 1MQ6:XLD and 1FJS:Z34. It is also possible that the knownfavorable electrostatic interaction between 1FJS:Z34 and the fXa S4pocket, which was not described by the displaced-solvent functional,contributed to 1FJS:Z34 being an outlier in this data set.

D. Cross Testing of the Trained Displaced Solvent Density Functionalsfor Factor Xa

The transferability of the parameters trained on the set of 31congeneric inhibitor pairs to the set of 28 crystal structure ligands(Table 6) was determined. The optimized 3- and 5-parameter functionalstrained on the set of 31 congeneric inhibitor pairs each had R² valuesof 0.17 when predicting the relative binding affinities of the 28crystal structure ligands to fXa. The functionals performed poorlybecause the values of the parameters obtained from training to the setof congeneric pairs typically predicted the difference in bindingaffinity between crystal structure pairs to be much too large (oftengreater than 10 kcal/mol). The reason for this can be subtle: typicallyit can be that the tightest binding compound of a series will becrystallized, and even then it can be typically crystallized if it bindswith a submicromolar affinity. Thus, if a ligand displaces a sub-optimalportion of the active site solvent density, then it, by construction,can become a crystallized ligand if it is possible to tune the othercontributions to the free energy (ligand entropy, ligand desolvationfree energy, protein ligand interaction energy, etc.) to offset thissuboptimal active-site-solvent evacuation, resulting in the neededsubmicromolar affinity. So the magnitude of the contributions predictedby the displaced-solvent functionals can be qualitatively correct, butthe other terms not described by the functional systematically offsetthem.

A contrast to this result was found when the 3- and 5-parameterfunctionals trained on the set of 28 crystal structure ligands were usedto predict the binding affinity differences of the set of 31 congenericinhibitor pairs. The 3- and 5-parameter functionals trained on a set ofcrystal structure ligands predicted the binding affinity differences ofthe set of 31 congeneric inhibitor pairs with R² values of 0.53 and0.59, respectively. This result suggested the functional form of thedisplaced-solvent functional can have fundamental features that lendthemselves to ranking the binding affinities of compounds that differ bydeletions of atoms—i.e., as long as the chosen parameters are physicallyreasonable, the performance of the functional over congeneric sets ofthis kind can be quite good.

E. Development and Testing of the Displaced-Solvent Functional on theSet of Cyclin Dependent Kinase 2 Congeneric Inhibitor Pairs

In light of the excellent performance of the described subject matter atdescribing the binding thermodynamics of fXa congeneric inhibitor pairs,two critical questions remained: (1) would the functional form of theDSF be appropriate to describe the binding thermodynamics of congenericinhibitor pairs binding to other protein receptors, and (2) would thereexist a consensus parameterization of the DSF that would well describethe binding thermodynamics of congeneric ligand pairs binding to anarbitrary protein receptor. To investigate the first outstanding issue,the binding thermodynamics of CDK2 and its small molecule inhibitors wasinvestigated.

The data for each hydration site identified in the CDK2 active site ispresented in Table 7 and the resulting hydration site map is depicted inFIG. 13. This hydration site map elucidated several features of theexperimentally known SAR of the CDK2 ligands. The active site of CDK2 isroughly planar and is typically bound by aromatic planar ligands. Theactive site pocket is lined above and below the plane of FIG. 13 byhydrophobic groups, and along the circumference of the plane by hydrogenbonding groups of the protein. As depicted in FIG. 13, the backbonehydrogen bonding sites Glu82, Phe83, and Leu84 are at the rear “hinge”region of the pocket, and charged residues Glu52, Lys34, Asp 146, Asp87,and Lys90 are at the front opening of the pocket. The hydration site mapgenerated by the described subject matter correctly identifies that thehydrophobic enclosure above and below the plane of the active site, asdepicted in FIG. 13, causes the active site solvent to be quiteenergetically depleted, as shown by the large number of gray hydrationsites, which creates a high affinity for hydrophobic ligand groups inthat region. The described subject matter also correctly identifies thatthose hydration sites solvating the hydrogen bonding sites in the hingeregion of the active site lined by Glu82, Phe83, and Leu84 areespecially favorable to evacuate. Hydration sites 3 and 5 are especiallynotable here, since nearly all CDK2 ligands that bind with high affinitymake hydrogen bonds with residues Glu82 and Leu84, which are solvated bythese hydrations sites. The described subject matter makes clear whythis is the case. The solvent hydrating hydrogen bonding sites of Glu82and Leu84 has very high excess chemical potential, thus favoring itstransfer to the bulk fluid when evacuated by a cognate ligand.

A dataset of 47 congeneric inhibitor pairs of CDK2 (see subsection II.C)(Table 8) was prepared. These 47 congeneric inhibitor pairs were pairsof CDK2 ligands that differed by at most three chemical groups and wereprepared by a procedure analogous to that used to assemble the set of 31congeneric inhibitor pairs of fXa. The parameters of thedisplaced-solvent functionals were optimized to reproduce theexperimentally measured differences in binding affinity between each ofthese congeneric ligand pairs. The error of the resulting functionalswere also estimated with leave-one-out cross validation. The resultingvalues of the parameters can be found in table 9 and plots of thepredicted differences in binding free energy versus the experimentalvalues are shown in FIGS. 14 and 15. The agreement of the predictions ofthe functionals with the experimental data was favorable: the Pearsoncorrelation coefficient (R²) was 0.53 and 0.54 for the 3-parameter and5-parameter functionals, respectively. Under leave-one-outcross-validation, the R² value degraded to 0.37 and 0.33, respectively.The agreement for fXa was more favorable largely due to the tendency ofthe charged side chains at the front of the CDK2 pocket to reorganize toaccommodate the particular bound ligand. However, from the reasonablygood numerical agreement observed over the 5 kcal/mol free energy rangeof modifications plotted in FIGS. 14 and 15, this techniquedifferentiated modifications that make large contributions to thebinding affinity from modifications that merely make small contributionsto the binding affinity for the CDK2 test system. This suggests that thepresent subject matter has sufficient flexibility in its functional formto describe the binding thermodynamics of ligands to a variety ofprotein receptors. It also directly motivates a study of whether or nota transferable parameterization of the DSF might be obtained that wouldhave good predictive power for both CDK2, fXa, and other proteins.

F. Performance of the Displaced Solvent Functional for the Set of AllFactor Xa and Cyclin Dependent Kinase 2 Congeneric Inhibitor Pairs

To test whether or not a transferable parameterization of the DSF mightexist that would have predictive utility for both the fXa and CDK2systems, the 3-parameter and 5-parameter functionals were trained to thecombined set of 31 fXa and 47 CDK2 congeneric inhibitor pairs. Thefunctional was trained with the aforementioned techniques and estimatedthe error of the resulting functionals with leave-one-out crossvalidation. The resulting values of the parameters can be found in Table10 and plots of the predicted differences in binding free energy versusthe experimental values are shown in FIGS. 16 and 17. The agreement ofthe predictions of the functionals with the experimental data wasfavorable in light of the fact that it is spread across two verydifferent protein receptors. The Pearson correlation coefficient (R²)for the combined data set was 0.57 and 0.60 for both the 3-parameter and5-parameter functionals. Under leave-one-out cross-validation, the R²value merely degraded to 0.52 and 0.51, respectively. Importantly, thehigh correlation is not due to one system being very well modeled andthe other being poorly modeled. The consensus parameterization of the3-parameter and 5-parameter functionals had R² values 0.64 and 0.65 overthe fXa data, and R² values 0.50 and 0.51 over the CDK2 data. Thus, theconsensus fit to both the CDK2 and fXa data merely marginally degradesthe performance of the functionals for either system. Given the highratio of data point to parameters, approximately 20:1, it is notsurprising that these findings are quite robust under cross validationand reparameterization. This suggests quite strongly the physicalpicture suggested by the DSF—i.e., the molecular description of thethermodynamics of the displaced solvent is of crucial importance tounderstanding the binding affinity of congeneric ligands binding to agiven receptor—is fundamentally correct.

Results suggest that the expulsion of active site water impactsprotein-ligand binding affinities in 2 ways: (1) hydrophobic ligandgroups that displace water from energetically unfavorable(hydrophobically enclosed) environments contribute enthalpically sincethe water molecules will make more favorable interactions in the bulkfluid; and (2) ligand groups that displace entropically structuredsolvent contribute even when the solvent interacts favorably with theprotein since well-designed ligands will recapture the protein-waterinteraction energy. Congeneric inhibitor pairs 2J4I:38-2J4I:GSJ and2BMG:25-2BMG:I1H are particularly clear examples where the expulsion ofactive site water that solvates an energetically unfavorable environmentleads to large favorable contributions to the binding free energy. Incontrast, congeneric pair 1MQ5:XLC-1MQ6:XLD offered an example of theexpulsion of water from a hydration site with a favorable interactionenergy and unfavorable excess entropy. The expulsion of water from thishydration site was found to be favorable, by the empirical criteria,presumably because the ligand group that displaces this water does areasonably good job recapturing the interaction energy of the solventwith the protein with less entropic cost. Congeneric inhibitor pair2BQ7:IID-2BQW:IIE illustrated that these two solvent categories,energetically unfavorable and entropically unfavorable, are by no meansmutually exclusive and that the evacuation of solvent from the proteinactive site will often make both entropic and enthalpic contributions tothe binding free energy. Important to the analysis is the assumption ofcomplementarity—that is, that the difference between the water-proteinenergetic interactions and the ligand-protein interactions was expectedto be small. This assumption is valid when the ligands form hydrogenbonds with the protein where appropriate and hydrophobic contactsotherwise; however, the congeneric ligand pair 1NFX:RDR/1NFW:RRRillustrated ligands that violate this hypothesis will often bemistreated by the technique. This has relevance to modern drug designsince it suggests that it is misleading to look at particular crystalwaters as favorable or unfavorable to displace, as is often done instructure based drug design. Instead, it can be more productive toconsider how thermodynamically favorable displacing a crystal water willbe when it is displaced by a complementary chemical group of a ligand.

The empirical functionals developed were suited to quantifying thecontributions to the free energy of binding due to the ligand evacuatingenergetically unfavorable and entropically structured solvent for theset of congeneric pairs. It was able to differentiate thosemodifications to an existing ligand scaffold that made smallcontributions to the binding affinity of the complex from thosemodifications that made large contributions over a 6 kcal/mol range.Although these functionals can be readily tuned to well describe thebinding thermodynamics for a single system, they can also be tuned tocorrectly describe the binding thermodynamics of several unrelatedsystems with high accuracy, suggesting that the parameters derived insection III.F can be readily transferable to other protein receptorsystems aside from those studied here.

In their present form, the 3- and 5-parameter functionals can be usefulto lead optimization, since the functionals appeared to well describethe thermodynamics of adding small chemical groups to a given ligandscaffold that are complementary to the protein surface. The performanceof the functionals on the set of 28 crystal structure ligands suggeststhat terms of this type make large contributions to binding; however,these functionals should not be used as a stand alone tool forcomputational screening of chemically diverse compounds. The reason forthis can be apparent: the displaced-solvent functionals presented hereneglect several terms which will vary considerably over sets ofchemically diverse ligands. These terms include the protein-ligandinteraction energy, ligand solvation free energy, ligand configurationalentropy, and the protein-reorganization free energy. Thus, a functionaldesigned for computational screening can include additional termsdescribing these types of contributions to the free energy in additionto those contributions captured by the displaced-solvent functional.

In one embodiment, a clustering algorithm was designed to partition thedisplaced solvent density into spheres with optimal convergenceproperties for the inhomogeneous solvation theory analysis. Thisalgorithm cycles through the positions of the oxygen atom of every watermolecule found in the active site solvent density distribution and findsthe position that has the greatest number of water neighbors within a 1Å radius. This position was denoted a hydration site and all of theoxygen positions within 1 Å of it in the solvent density distributionwas removed. This process is then repeated, cycling through theremaining positions. This loop is terminated when the clusteringalgorithm identifies a hydration site with a water-oxygen occupancy lessthan twice the expected value of a 1 Å radius sphere in the bulk fluid.

In one embodiment, in pseudocode, the clustering algorithm wasimplemented (hereinafter “Algorithm I”) as

BEGIN CLUSTERING LOOP     FOR ALL water molecules currently in theactive site     solvent distribution     COMPUTE number of neighboringwaters within 1 Å     (oxygen to oxygen distance)     SAVE coordinatesof the water with the greatest number of     neighbors as a cluster    REMOVE all waters within 1 Å of this cluster center from     thesolvent density distribution     IF the number of waters in this clusteris less than twice bulk     density,     THEN terminate and delete thelast saved cluster     ELSE repeat the loop END LOOP

In one embodiment, the calculation of the excess entropies of the watermolecules occupying the hydration sites was performed by numericallyintegrating an expansion of the entropy in terms of orientational andspatial correlation functions. The translational contribution wasnumerically integrated to the excess entropy in spherical coordinatesusing a length of 0.03 Å along r, 15° along θ, and 30° along φ; and theorientational contribution was numerically integrated with 10° alongeach Euler angle.

In one embodiment, in pseudocode, one implementation (hereinafter“Algorithm II”) of the calculation of the translational component of theexcess entropy of the water molecules in a given cluster was

COMPUTE r, θ, and φ for each water oxygen in the cluster (the center ofthe cluster is used as the origin HISTOGRAM the observed r, θ, and φvalues in bins of 0.03 Å along r, 15° along θ, and 30° along φ and saveas N_(r,θ,φ) DO r = 0, 1.0 Å, dr      DO θ=0, 180°, dθ         DO φ=0,360°, dφ             N_(r,θ,φ)=N_(r,θ,φ)/(r+dr/2){circumflex over( )}2*sin(θ+dθ/2)*dr*dθ*dφ*             #frames*ρ             IFN_(r,θ,φ) ≠ 0 THEN S^(e) _(trans)=[N_(r,θ,φ)log(N_(r,θ,φ))]*            (r+dr/2){circumflex over ( )}2*sin(θ+dθ/2)*dr*dθ*dφ+ S^(e)_(trans)         END DO      END DO END DO S^(e) _(trans)=−kρ*S^(e)_(trans)

In one embodiment, the integration of the orientational component of theexcess entropy of the waters occupying a given hydration site isperformed semi-analytically by way of a mixed quaternion/Euler angleformalism. Each water oxygen within the cluster is translated to acommon reference point and the hydrogens are moved accordingly. Thehydrogen to hydrogen mappings that allow for the smallest rotation of awater in the cluster onto the coordinates of a reference water aredetermined by a hydrogen to hydrogen distance criterion, i.e., H₁->H_(a)and H₂->H_(b) should be minimal. The quaternion that maps H₁->H_(a)using the rotational axis orthogonal to the OH₁ and OH_(a) bond vectorsis then determined. This rotation is applied and a second quaternion isdetermined that will rotate H₂->H_(b) using the analogous orthogonalaxis. These two quaternions are combined using the analyticalcombination rules to derive the single “master” quaternion that rotatesH₁->H_(a) and H₂->H_(b) simultaneously. It should be noted thecalculation of this quaternion can be done in a single stage using theaxis of rotation orthogonal to vectors H₁H_(a), and H₂H_(b), but thistechnique suffered numerical instabilities when the vectors were closeto parallel; so, the two-stage technique can be used. From the “master”quaternion the three Euler angles that rotate a cluster water onto thereference water were analytically extracted. This process was repeatedfor all waters and the rotational correlation function was determinedfrom the distribution of the Euler angles and used to numericallyintegrate the one-body rotational term using a 10° discretization.

In one embodiment, In pseudocode, one implementation (hereinafter“Algorithm III”) of the calculation of the orientational component ofthe excess entropy of the water molecules in a given cluster was

TRANSLATE each water oxygen to the origin, move all water hydrogensappropriately SAVE the hydrogen positions of the first water in thedistribution as the reference water, label H_(a) and H_(b) LOOP FOR EACHwater in the cluster     COMPUTE the mapping of hydrogens H₁ and H₂ ofthe cluster water onto the reference water such that dist(H₁,H_(a)) anddist(H₂,H_(b)) are minimal     COMPUTE the axis of rotation A=H₁ × H_(a)where H₁ and H_(a) are the vectors from the origin to the relevanthydrogen atom     COMPUTE the angle of rotation α=−cos⁻¹[ (H₁*H_(a)) /(||H₁|| ||H_(a)||) ]     COMPUTE the elements of quaternion q as        q⁰=cos((α/2)         q¹=A₁*sin((α/2)         q²=A₂*sin((α/2)        q³=A₃*sin((α/2)     ROTATE H₁ and H₂ with this quaternion via        H_(j) ⁽¹⁾=(q⁰q⁰+q¹q¹−q²q²−q³q³)H_(j) ⁽¹⁾+2(q¹q²+q⁰q³)H_(j)⁽²⁾+2(q¹q³+q⁰q²)H_(j) ⁽³⁾         H_(j) ⁽²⁾=2(q¹q²+q⁰q³)H_(j)⁽¹⁾+(q⁰q⁰−q¹q¹+q²q²−q³q³)H_(j) ⁽²⁾+2(q²q³+q⁰q¹)H_(j) ⁽³⁾         H_(j)⁽³⁾=2(q¹q³+q⁰q²)H_(j) ⁽¹⁾+2(q²q³+q⁰q¹)H_(j) ⁽²⁾+(q⁰q⁰−q¹q¹−q²q²+q³q³)H_(j) ⁽³⁾     COMPUTE A=H_(a)/ ||H_(a)||     COMPUTE V₁=H_(b)× A     COMPUTE V₂=H₂ × A     COMPUTE α=cos⁻¹[ (V₁*V₂) / (||V₁|| ||V₂||)]     COMPUTE V₃=V₁ × V₂     IF V₃*A < 0.0 THEN α=−α     COMPUTE theelements of quaternion w as         w⁰=cos(α/2)         w¹=A₁*sin(α/2)        w²=A₂*sin(α/2)         w³=A₃*sin(α/2)     COMPUTE the quaternioncross product e=q × w     COMPUTE renormalize this quaternion as e= e/||e||     COMPUTE the Euler angles of the rotation described byquaternion e as         (=tan⁻¹(2(e²e³+e⁰e¹)/(1−2(e¹e¹+e²e²)))        θ=sin⁻¹(2(e¹e³+e⁰e²))        ψ=tan⁻¹(2(e¹e²+e⁰e³)/(1−(e²e²+e³e³)))     SAVE the Euler anglesfor each water END LOOP HISTOGRAM the observed φ, θ, and ψ values in 10°bins along each Euler angle and save as N_(φ,θ,ψ) DO φ=0, 180°, dθ      DO θ=0, 180°, dθ           DO ψ=0, 180°, dθ               IFN_(φ,θ,ψ) ≠ 0 THEN S^(e) _(rot)=[N_(φ,θ,ψ) log(N_(φ,θ,ψ))]*dφ *dθ*dψ+S^(e) _(rot)           END DO       END DO END DO S^(e)_(trans)=−[(k*#wat)/((*#frames)]S^(e) _(trans)

A dual color code is used to visualize the hydration sites against thebackdrop of the protein so that the structural and thermodynamicproperties, both energetic and entropic, of the hydration sites areimmediately apparent in one summary figure. This visualization wasperformed by writing a PDB formatted file of the hydration sites. Inthis file replaced the occupancy and beta values with the excess entropyand average system interaction energy values for each hydration site.Many existing protein structure visualization programs contain theability to visualize and color parts of the system differently basedupon the specified beta and occupancy values. This allowed for thevisualization of the hydration sites against the backdrop of the proteinwith the excess entropy and interaction energy related to the viewer byway of a color code.

Each hydration site in the active site volume was chosen to be viewed ifeither its average system interaction energy or its excess entropy wereless favorable than some set of predefined cutoffs. If a hydration sitewas displayed merely because its average system interaction energy wasless favorable than some cutoff, then this hydration site was visualizedin silver; if a hydration site was displayed merely because its excessentropy was less favorable than some cutoff, then this hydration sitewas visualized in green; and if a hydration site was displayed becauseboth its average system interaction energy and excess entropy were lessfavorable than some cutoffs, then this hydration site was visualized inpurple. This visualization scheme allowed rapid viewing of severalthermodynamic properties of the hydration sites simultaneously. Anexample of the resulting graphic can be found in FIG. 2, with examplecutoff values suggested by FIG. 1.

The visualizations of the hydration sites were performed similarly hereas above. However the differences in the structures of two congenericligands was also displayed by visualizing two identical replicas of theprotein and hydration sites next two each other where one congenericligand was docked in the active site of protein replica 1 and anothercongeneric ligand was docked in the active site of protein replica two.Examples of the resulting graphics produced by this technique can beseen in FIGS. 5-10.

An empirical technique is use to determine whether or not and to whatdegree the solvent in a given hydration site is displaced from receptorby the bound ligand. This technique uses the Cartesian distances betweenthe ligand atoms and the hydration sites to develop an approximatedescription of the evacuation of solvent from the hydration sites by aligand. This technique works by noting that as a ligand atom approachesa hydration site the Van der Waals surface of the ligand atom will beginto displace solvent from the hydration site once the solvent in thehydration site and the ligand come in to contact; as the ligand atommoves closer to the hydration, more solvent will be evacuated. The formof the functional was a sum over ligand heavy atoms and a sum overhydration sites. The functional itself used to describe this effect was

$\begin{matrix}{{\Delta\; G_{lig}} = {\sum\limits_{{lig},{hs}}\;{\Delta\;{G_{rwd}\left( {E_{hs},S_{hs}^{e}} \right)}\left( {1 - \frac{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}{R_{co}}} \right){\Theta\left( {R_{co} - {{{\overset{\rightharpoonup}{r}}_{lig} - {\overset{\rightharpoonup}{r}}_{hs}}}} \right)}}}} & (4)\end{matrix}$where ΔG_(bind) was the predicted binding free energy of the ligand,ΔG_(rwd)(E_(hs),S^(e) _(hs)) was the free energy contrition ofevacuating the solvent from the hydration site hs to the bindingaffinity of the ligand, R_(co) was the distance cutoff for a ligand atombeginning to displace a hydration site, and Θ was the Heaviside stepfunction. Once a particular choice of ΔG_(rwd)(E_(hs),S^(e) _(hs)) isspecified, the free parameter R_(co) is tuned to reproduce the knownbinding thermodynamics of some large set of protein ligand complexes.Several choices of the function ΔG_(rwd)(E_(hs),S^(e) _(hs)) arepossible. The most obvious is the transfer free energy of the solventmolecules in the hydration from the active site to the bulk fluid andcorresponds to a arrangement of hard spheres in the shape of the ligandevacuating the solvent. This choice however neglect considering that“real” ligand will not interact with the proteins hard spheres. Thus theinteractions of the protein and the ligand can be implicitly accountedfor by using other choices for the function ΔG_(rwd)(E_(hs),S^(e)_(hs)).

As noted elsewhere, in one embodiment, the functional that was developedto describe the binding affinities of the ligands as a function of thethermodynamic properties hydration sites was eq. 1. The form of thefunctional was a sum over ligand heavy atoms and a sum over hydrationsites. Each time a ligand heavy was found within some parameterizeddistance of a hydration site with an interaction energy or excessentropy predicted to be favorable to evacuate by some fit empiricalcriteria, an additive contribution was summed.

In pseudocode, this functional was implemented as

LOOP FOR EACH hydration site    LOOP FOR EACH heavy atom of the ligand       COMPUTE the distance between the particular hydration site (HS)and the ligand heavy atom (LA), i.e.,        IF dist(HS,LA)           IF S^(e) _(hs) > S_(co) THEN               G_(rwd,HS)=G_(rwd.HS)−T*S_(rwd)*               (1−(dist(HS,LA)/                R_(co)))            ENDIF            IF E_(hs) > E_(co) THEN                G_(rwd.HS)=               G_(rwd.HS)+E_(rwd)*(1−(dist(HS,LA)/               R_(co)))            END IF        END IF    END LOOP   G_(bind)= G_(bind)+G_(rwd.HS) END LOO

Table and Figure Captions

TABLE 1 Calculated thermodynamic and local water structure data for eachof the 43 hydration sites that were identified by clustering the factorXa active site solvent density distribution. Hyd. Site Occupancy −TSe(kcal/mol) E (kcal/mol) #nbrs #HBnbrs % HB Exposure Neat 1385.00 N/A*−19.67 5.09 3.53 0.69 1.00 1 9347.00 4.00 −20.34 1.54 1.30 0.84 0.30 29062.00 3.91 −22.59 3.13 1.99 0.64 0.61 3 8425.00 2.61 −20.85 3.45 2.270.66 0.68 4 8383.00 2.93 −19.55 3.12 2.79 0.89 0.61 5 8157.00 3.24−23.18 2.52 1.88 0.75 0.50 6 8123.00 3.20 −21.86 3.62 2.24 0.62 0.71 78116.00 3.37 −21.82 3.22 2.12 0.66 0.63 8 8081.00 2.74 −22.73 3.05 2.390.78 0.60 9 7257.00 2.13 −19.38 4.30 2.76 0.64 0.84 10 7172.00 2.52−21.04 3.75 2.85 0.76 0.74 11 6886.00 2.05 −20.71 3.41 2.24 0.66 0.67 126815.00 2.28 −16.93 1.62 1.49 0.92 0.32 13 6238.00 1.72 −17.88 2.72 2.050.75 0.53 14 6081.00 1.95 −19.89 2.58 2.11 0.82 0.51 15 5441.00 1.83−22.62 4.66 3.63 0.78 0.92 16 5078.00 1.51 −20.01 3.30 2.56 0.78 0.65 174919.00 1.33 −17.04 2.45 1.78 0.73 0.48 18 4887.00 1.35 −17.74 3.38 2.460.73 0.66 19 4466.00 1.20 −19.48 4.11 2.77 0.67 0.81 20 4386.00 1.37−22.14 3.69 2.79 0.76 0.72 21 4356.00 1.23 −18.50 3.75 2.67 0.71 0.74 224241.00 1.22 −20.27 3.72 2.63 0.71 0.73 23 4189.00 1.13 −19.58 3.87 2.840.73 0.76 24 4170.00 1.17 −19.64 3.69 2.51 0.68 0.72 25 4137.00 1.12−20.85 4.61 2.59 0.56 0.91 26 4067.00 1.07 −20.19 4.23 3.09 0.73 0.83 274046.00 1.03 −20.72 4.37 3.48 0.80 0.86 28 3921.00 1.10 −16.74 2.66 2.000.75 0.52 29 3833.00 1.03 −21.44 4.27 2.57 0.60 0.84 30 3793.00 1.04−21.97 4.05 2.68 0.66 0.80 31 3786.00 0.99 −20.00 4.70 3.39 0.72 0.92 323686.00 0.99 −22.61 4.48 2.69 0.60 0.88 33 3618.00 1.00 −20.46 4.34 2.560.59 0.85 34 3570.00 0.95 −19.75 4.36 2.92 0.67 0.86 35 3312.00 0.90−24.24 4.41 2.74 0.62 0.87 36 3296.00 0.84 −19.66 4.06 2.66 0.66 0.80 373152.00 0.79 −18.87 4.57 3.15 0.69 0.90 38 3094.00 0.73 −19.09 4.70 3.250.69 0.92 39 3089.00 0.92 −21.61 3.55 2.55 0.72 0.70 40 3007.00 0.79−19.96 4.20 2.79 0.67 0.82 41 3003.00 0.78 −20.41 3.71 2.70 0.73 0.73 422862.00 0.73 −19.26 4.72 3.28 0.69 0.93 43 2791.00 0.75 −20.93 3.98 2.840.71 0.78 *The truncated expansion of the excess entropy used merelyincluded the first order terms. The first order excess entropic term forall neat fluids is strictly zero, however the second order and largerterms can be quite large. The occupancy was the number of water oxygenatoms found occupying a given hydration site during the 10 ns ofmolecular dynamics simulation, −TS^(e) is the excess entropiccontribution to the free energy calculated from a truncated expansion ofthe excess entropy in terms of correlations in the single particletranslational and rotational density, E is average energy of interactionof the water molecules in a given hydration site with the rest of thesystem, the #nbrs value is the average number of neighboring watersfound within a 3.5 Å oxygen atom to oxygen atom distance from a wateroccupying the specified hydrations site, the #HBnbrs value is the is theaverage number of neighboring water oxygens found within a 3.5 Ådistance from the water oxygen occupying the specified hydrations sitethat make a less than 30° oxygen-oxygen-hydrogen hydrogen bonding anglewith this water, the % HB value is the #HBnbrs/#nbrs fraction, and theexposure value is the #nbrs value divided by the bulk #nbrs value foundin the bulk fluid.

TABLE 2 Inhibition data for the congeneric ligand pairs binding tofactor Xa and the predicted activity differences from the trained3-parameter and 5-parameter displaced-solvent functionals. When a ligandwas taken from a solved crystal structure, the ligand was designated“(pdb id):(ligand residue name)”; and when the ligand was built fromcongeneric series data, the ligand was designated “(template pdbid):(molecule number in the reporting publication)”. ΔΔG_(exp) ΔΔG_(3p)ΔΔG_(5p) Initial Final (kcal/ (kcal/ (kcal/ Ligand Initial LigandStructure Ligand Final Ligand Structure mol) mol) mol) 1MQ5:XLC

1MQ6:XLD

−2.94 −2.85 −2.54 1NFU:RRP

1NFY:RTR

−1.56 −2.56 −2.98 1NFX:RDR

1NFW:RRR

−0.59 1.35 0.94 2BMG:25

2BMG:28

−0.62 −0.61 −0.62 2BMG:25

2BMG:I1H

−1.05 −1.31 −1.31 2BMG:28

2BMG:I1H

−0.43 −0.70 −0.69 1KYE:3

1KYE:2

−0.90 −2.05 −2.34 1V3X:56

1V3X:57

−0.59 −1.15 −1.12 1V3X:60

1V3X:56

−0.19 0.00 0.00 1V3X:56

1V3X:D76

−0.54 −1.15 −1.12 1V3X:60

1V3X:57

−0.79 −1.15 −1.12 1V3X:D76

1V3X:57

−0.05 0.00 0.00 1V3X:60

1V3X:D76

−0.74 −1.15 −1.12 2BQ7:IID

2BQW:IIE

−2.01 −1.73 −1.95 2BQ7:IID

2BOH:IIA

−2.01 −1.80 −2.09 2BQW:IIE

2BOH:IIA

0.00 −0.07 −0.13 1Z6E:11a

1Z6E:43

−0.09 0.04 0.39 1Z6E:43

1Z6E:IK8

−0.68 −0.04 −0.45 1Z6E:11a

1Z6E:IK8

−0.77 0.00 −0.06 1G2L:T87

1G2M:R11

−0.21 0.79 0.20 1KSN:5c

1KSN:FXV

−0.13 −0.87 −0.78 2FZZ:4QC

2G00:5QC

−1.06 −1.70 −1.68 1LQD:107

1LQD:CMI

−3.93 −2.52 −2.48 1LQD:108

1LQD:46

−3.09 −2.52 −2.48 1F0R:815

1F0S:PR2

−0.12 0.09 0.14 2J4I:33

2J4I:GSJ

−0.82 0.00 0.00 2J4I:32

2J4I:GSJ

−4.93 −4.87 −4.83 2J4I:38

2J4I:GSJ

−6.26 −4.87 −4.83 2J4I:32

2J4I:33

−4.11 −4.87 −4.83 2J4I:38

2J4I:33

−5.44 −4.87 −4.83 2J4I:38

2J4I:32

−1.33 0.00 0.00

TABLE 3 The optimized parameters for the 3-parameter and 5-parameterforms of the displaced-solvent functional trained to reproduce theexperimentally measured differences in binding affinity of 31 fXacongeneric pairs. R_(co) −TS_(rwd) E_(co) TS_(co) Parameters: (Å)E_(rwd) (kcal/mol) (kcal/mol) (kcal/mol) (kcal/mol) version 3p 2.8 −0.99−0.99 −18.91 1.34 version 5p 3.27 −0.92 −0.66 −18.89 1.34

TABLE 4 Inhibition data for the 28 ligands extracted from solved crystalstructures binding to factor Xa and the predicted activity differencesfrom the trained 3-parameter and 5-parameter displaced-solventfunctionals. Each ligand was designated “(pdb id):(ligand residue name)”Ligand ΔG_(exp) (kcal/mol) ΔG_(3p) (kcal/mol) ΔG_(5p) (kcal/mol)2BOK:784 −9.39 −6.12 −7.24 2J2U:GSQ −9.61 −7.26 −8.60 2BQ7:IID −9.62−7.78 −8.77 1G2L:T87 −9.88 −6.86 −8.93 2J34:GS5 −10.00 −6.73 −7.80IG2M:R11 −10.09 −6.54 −8.42 1KYE:RUP −10.37 −7.47 −8.88 1F0R:815 −10.45−6.26 −7.53 1F0S:PR2 −10.57 −6.39 −7.77 2BMG:I1H −10.57 −8.49 −9.341NFU:RRP −10.57 −6.98 −8.50 2J38:GS6 −10.67 −6.94 −8.06 1LQD:CMI −10.98−8.22 −9.30 2CJI:GSK −11.22 −7.48 −8.52 2BQW:IIE −11.63 −8.07 −9.161NFX:RDR −11.63 −7.58 −8.97 2BOH:IIA −11.63 −8.61 −9.72 1NFY:RTR −12.12−7.47 −8.89 1NFW:RRR −12.22 −7.21 −8.46 1MQ5:XLC −12.28 −8.53 −9.582J4I:GSJ −12.28 −7.98 −9.33 1EZQ:RPR −12.34 −8.41 −9.91 1KSN:FXV −12.82−8.10 −9.39 1Z6E:IK8 −13.26 −9.90 −11.55 2FZZ:4QC −13.29 −9.93 −11.331FJS:Z34 −13.59 −7.04 −8.75 2G00:5QC −14.36 −9.98 −11.44 1MQ6:XLD −15.22−8.66 −9.75

TABLE 5 The optimized parameters for the 3-parameter and 5-parameterforms of the displaced-solvent functional trained on the set of 28ligands taken from factor Xa crystal structures. R_(co)/ −TS_(rwd)/E_(co)/ TS_(co)/ Parameters: (Å) E_(rwd)/(kcal/mol) (kcal/mol)(kcal/mol) (kcal/mol) version 3p 2.8 −0.42 −0.42 −16.87 0.99 version 5p2.52 −0.2 −0.44 −19.62 1.18

TABLE 6 Results of the 3- and 5-parameter displaced-solvent functionalstrained on the set 31 congeneric pairs applied to the 28 ligand set; andresults of the 3-parameter and 5-parameter displaced- solventfunctionals trained on the 28 ligand set applied to the set of 22congeneric pair subset that excludes congeneric pairs where both pairsare crystal structure ligands. 3 Parameter Form 28 Crystal Structure 31Congeneric Pairs Ligands Status Trained Tested RMSD/(kcal/mol) 0.76 3.19AAE/(kcal/mol) 0.60 2.60 R² 0.81 0.17 5 Parameter Form 28 CrystalStructure 31 Congeneric Pairs Ligands Status Trained TestedRMSD/(kcal/mol) 0.75 3.09 AAE/(kcal/mol) 0.58 2.53 R² 0.81 0.17 3Parameter Form 28 Crystal Structure Ligands 22 Congeneric Pairs StatusTrained Tested RMSD/(kcal/mol) 1.56 1.99 AAE/(kcal/mol) 1.17 1.24 R²0.48 0.53 5 Parameter Form 28 Crystal Structure Ligands 22 CongenericPairs Status Trained Tested RMSD/(kcal/mol) 1.51 1.88 AAE/(kcal/mol)1.18 1.17 R² 0.50 0.59

TABLE 7 Calculated thermodynamic and local water structure data for eachof the 43 hydration sites that were identified by clustering the CDK2active site solvent density distribution. Hyd. Site Occupancy −TS^(e)(kcal/mol) E (kcal/mol) #nbrs #HBnbrs % HB Exposure Neat 1385 N/A*−19.67 5.09 3.53 0.69 1 1 9608 4.23 −20.66 1.70 0.88 0.33 0.25 2 93274.05 −22.16 2.86 1.37 0.56 0.39 3 9062 3.50 −18.57 2.12 1.89 0.42 0.53 48570 3.19 −22.97 3.33 2.03 0.65 0.57 5 7983 2.67 −18.23 2.49 2.09 0.490.59 6 7669 2.43 −23.19 4.63 2.60 0.91 0.74 7 7646 2.62 −18.31 3.29 2.830.65 0.80 8 7467 2.56 −24.27 3.97 1.82 0.78 0.51 9 7133 2.94 −24.32 3.351.85 0.66 0.52 10 6888 1.98 −18.6 3.45 3.03 0.68 0.86 11 6869 2.41−24.14 2.98 1.90 0.58 0.54 12 6167 1.91 −18.63 2.65 2.20 0.52 0.62 135719 1.66 −20.59 3.61 1.96 0.71 0.56 14 5719 1.66 −20.59 3.61 1.96 0.710.56 15 5584 1.52 −18.7 4.15 3.05 0.82 0.86 16 5552 1.77 −22.73 3.372.12 0.66 0.60 17 5411 1.60 −23.05 3.69 2.18 0.73 0.62 18 5021 1.45−23.14 3.33 1.95 0.65 0.55 19 4851 1.49 −23.09 4.71 2.72 0.93 0.77 204812 1.44 −21.11 3.86 2.73 0.76 0.77 21 4790 1.29 −19.93 4.07 2.85 0.800.81 22 4764 1.28 −17.7 3.83 2.96 0.75 0.84 23 4549 1.16 −19.24 3.933.16 0.77 0.90 24 4450 1.12 −19.35 3.82 2.96 0.75 0.84 25 3946 1.05−21.42 3.59 2.59 0.71 0.73 26 3715 1.01 −20.43 3.50 1.83 0.69 0.52 273687 1.06 −19.71 3.95 2.66 0.78 0.75 28 3676 1.01 −23.12 4.51 2.68 0.890.76 29 3608 1.03 −22.82 5.08 2.19 1.00 0.62 30 3580 0.95 −20.49 4.533.24 0.89 0.92 31 3523 0.91 −17 3.31 2.48 0.65 0.70 32 3388 0.91 −20.894.34 2.90 0.85 0.82 33 3303 0.86 −19.88 4.80 3.11 0.94 0.88 34 3348 0.93−21.86 4.37 2.39 0.86 0.68 35 3271 0.92 −22.85 5.06 2.64 0.99 0.75 363250 0.84 −17.2 3.53 2.75 0.69 0.78 37 3199 0.83 −19.86 4.34 3.51 0.850.99 38 3092 0.77 −19.01 4.58 3.26 0.90 0.92 39 3074 0.80 −19.71 4.733.37 0.93 0.95 40 3017 0.78 −18.24 4.37 3.14 0.86 0.89 41 3338 0.85−19.6 4.05 2.89 0.80 0.82 42 2953 0.77 −20.17 5.11 3.43 1.00 0.97 432950 0.88 −18.3 3.18 2.12 0.62 0.60 44 2848 0.85 −22.64 5.18 2.64 1.020.75 45 2815 0.73 −18.86 4.15 2.98 0.81 0.84 *The truncated expansion ofthe excess entropy used can merely include the first order terms. Thefirst order excess entropic term for all neat fluids is strictly zero,however the second order and larger terms will be quite large. Theoccupancy was the number of water oxygen atoms found occupying a givenhydration site during the 10 ns of molecular dynamics simulation,−TS^(e) is the excess entropic contribution to the free energy calcul-ated from a truncated expansion of the excess entropy in terms ofcorrelations in the single particle trans- lational and rotationaldensity, E is average energy of interaction of the water molecules in agiven hydration site with the rest of the system, the #nbrs value is theaverage number of neighboring waters found within a 3.5 Å oxygen atom tooxygen atom distance from a water occupying the specified hydrationssite, the #HBnbrs value is the is the average number of neighboringwater oxygens found within a 3.5 Å distance from the water oxygenoccupying the specified hydrations site that make a less than 30°oxygen-oxygen-hydrogen hydrogen bonding angle with this water, the % HBvalue is the #HBnbrs/#nbrs fraction, and the exposure value is the #nbrsvalue divided by the bulk #nbrs value found in the bulk fluid.

TABLE 8 Inhibition data for the congeneric ligand pairs binding to CDK2and the predicted activity differences from the trained 3-parameter and5-parameter displaced-solvent functionals. When a ligand was taken froma solved crystal structure, the ligand was designated “(pdb id):(ligandresidue name)”; and when the ligand was built from congeneric seriesdata, the ligand was designated “(template pdb id):(molecule number inthe reporting publication)”. Initial Final ΔΔG_(exp) ΔΔG_(3p) ΔΔG_(5p)Ligand Initial Ligand Structure Ligand Final Ligand Structure (kcal/mol)(kcal/mol) (kcal/mol) 1H1P:CMG

1H1S:4SP

−4.53 −4.05 −4.17 1KE9:LS5

1KE5:LS1

−0.1 −0.47 −0.38 1KE8:LS4

1KE5:LS1

−0.35 −0.30 −0.24 1OGU:8a

1OGU:ST8

−4.45 −2.33 −1.97 1OIU:N76

1OIY:N41

−0.71 −0.51 0.05 1OI9:2

1OIY:N41

−0.62 −0.70 −0.75 1OIU:N76

1OI9:N20

−0.66 −0.21 0.31 1OI9:2

1OI9:N20

−1.58 −0.40 −0.49 1OI9:2

1OIU:N76

−0.91 −0.19 −0.80 1PXJ:CK2

1PXL:CK4

−1.59 −2.22 −2.02 1PXJ:CK2

1PXK:CK3

−1.06 −0.95 −0.66 1PXM:11

1PXM:CK5

−0.17 −0.21 −0.24 1PXP:CK8

1PXM:CK5

−0.77 −0.25 −0.60 1PXP:CK8

1PXM:11

−0.6 −0.05 −0.36 1VYZ:N5B

1VYW:292

−1.23 0.80 0.65 1VYZ:1

1VYW:292

−2.21 −2.23 −1.94 1VYZ:1

1VYZ:N5B

−0.98 −3.03 −2.59 2B52:D42

2B52:8i

−0.41 0.34 0.19 2B53:14

2B53:D23

−0.55 −1.74 −1.83 2B53:42

2B53:D23

−0.65 −0.38 −0.25 2B53:42

2B53:14

−0.11 1.35 1.58 2B54:19m

2B54:22g

−0.08 0.00 0.00 2B54:D05

2B54:22g

−0.21 0.00 0.00 2B54:18i

2B54:22g

−0.32 0.00 0.00 2B54:18b

2B54:22g

−1.15 −0.28 −0.96 2B54:10i

2B54:22g

−1.37 −0.48 −1.02 2B54:D05

2B54:19m

−0.13 0.00 0.00 2B54:18i

2B54:19m

−0.24 0.00 0.00 2B54:18b

2B54:19m

−1.07 −0.28 −0.96 2B54:10i

2B54:19m

−1.29 −0.48 −1.02 2B54:18i

2B54:D05

−0.11 0.00 0.00 2B54:18b

2B54:D05

−0.94 −0.28 −0.96 2B54:10i

2B54:D05

−1.16 −0.48 −1.02 2B54:18b

2B54:18i

−0.83 −0.28 −0.96 2B54:10i

2B54:18i

−1.05 −0.48 −1.02 2B54:10i

2B54:18b

−0.22 −0.20 −0.06 2BHE:1

2BHE:BRY

−0.41 −0.43 −0.30 2BKZ:7h

2BKZ:SBC

−2.16 −1.08 −0.98 2BKZ:7x

2BKZ:SBC

−2.55 −1.71 −1.87 2BKZ:7x

2BKZ:7h

−0.39 −0.62 −0.88 2CLX:1b

2CLX:F18

−0.96 −0.99 −0.77 2FVD:35

2FVD:LIA

−0.51 0.00 0.00 2FVD:37

2FVD:LIA

−0.87 −0.07 0.00 2FVD:29

2FVD:LIA

−1.24 −0.07 0.00 2FVD:37

2FVD:35

−0.37 −0.07 0.00 2FVD:29

2FVD:35

−0.73 −0.07 0.00 2FVD:29

2FVD:37

−0.37 0.00 0.00

TABLE 9 The optimized parameters for the 3-parameter and 5-parameterforms of the displaced-solvent functional trained to reproduce theexperimentally measured differences in binding affinity of 47 CDK2congeneric pairs. E_(rwd) −TS_(rwd) E_(co) TS_(co) Parameters: R_(co)(Å) (kcal/mol) (kcal/mol) (kcal/mol) (kcal/mol) version 3p 2.8 −0.59−0.59 −19.17 1.97 version 5p 2.23 −0.54 −0.81 −19.54 1.95

TABLE 10 The optimized parameters for the 3-parameter and 5-parameterforms of the displaced-solvent functional trained to reproduce theexperimentally measured differences in binding affinity of 31 fXa and 47CDK2 congeneric pairs. R_(co) −TS_(rwd) E_(co) TS_(co) Parameters: (Å)E_(rwd) (kcal/mol) (kcal/mol) (kcal/mol) (kcal/mol) version 3p 2.8 −0.95−0.95 −18.22 1.66 version 5p 2.91 −1.33 −0.64 −18.58 1.71

The foregoing merely illustrates the principles of the disclosed subjectmatter. Various modifications and alterations to the describedembodiments will be apparent to those skilled in the art in view of theteachings herein. It will thus be appreciated that those skilled in theart will be able to devise numerous techniques which, although notexplicitly described herein, embody the principles of the disclosedsubject matter and are thus within the spirit and scope thereof.

1. A method of constructing a 3 dimensional hydration thermodynamics mapof a receptor, comprising: (a) calculating local statisticalthermodynamic properties of water molecules solvating the receptor,wherein the calculating comprises using a computer processor; and (b)visualizing the local statistical thermodynamic properties of the watermolecules as hydration sites against the backdrop of the receptor. 2.The method of claim 1, wherein the calculating comprises: (c) samplingconfigurations of the water solvating a receptor; and (d) extractingthermodynamic information about the solvating water from theconfigurations including: (i) automatically partitioning observed waterconfigurations into hydration sites, (ii) computing average systeminteraction energies of water molecules occupying the hydration sites,and (iii) computing excess entropies of the water molecules occupyingthe hydration sites.
 3. The method of claim 1, wherein the hydrationsites are displayed against the backdrop of the receptor when theenergetic and entropic properties are above or below one or more cutoffvalues.
 4. The method of claim 1, wherein a color code is used torepresent the energetic and entropic properties of the displayedhydration sites.
 5. The method of claim 1, further comprising: (e)visualizing a ligand in the receptor superimposed with the hydrationsites.